visuomotor adaptation in anisometropic amblyopia: a prism ... · visuomotor adaptation in...

Visuomotor Adaptation in Anisometropic Amblyopia: A Prism Adaptation Study

by

Jaime Cayla Sklar

A thesis submitted in conformity with the requirements for the degree of Masters of Science

Institute of Medical Science University of Toronto

© Copyright by Jaime Cayla Sklar 2015

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Visuomotor Adaptation in Anisometropic Amblyopia: A prism Adaptation Study

Jaime Cayla Sklar

Masters of Science

Institute of Medical Science

University of Toronto

2015

Abstract

The purpose of this investigation was to assess the impact of anisometropic amblyopia, a

neurodevelopmental disorder of vision, on sensorimotor control. This was accomplished by

adapting the manual motor system to a change in the spatial environment resulting from the

placement of bilateral 20 D left-shifting wedge prisms in front of the eyes. During prism

adaptation, participants initially missed the target in the direction of optical displacement but in

the presence of visual feedback and with repeated pointing, they were able to adapt to this

perturbation. Eleven visually-normal control participants and seven people with anisometropic

amblyopia were tested binocularly on this task. People with anisometropic amblyopia displayed

a significantly longer time course of adaptation than visually-normal controls, with higher

variability during the strategic recalibration phase of adaptation. It is suggested that increased

visual variability, temporal synchrony deficits and deficits in extra-striate visual processing in

amblyopia contribute to the above findings.

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Acknowledgments

I would like to take this opportunity to thank a number of people who helped see this study

through from beginning to end. If it was not for their contributions, support and kind words this

thesis would not be what it is today.

To my supervisor, Dr. Agnes Wong, I would like to extend a tremendous thank you for your

support over the last two years. Being given the opportunity to work in your Eye Movement and

Vision Neuroscience Laboratory has been a true blessing; I have learned and grown a lot, not

only about the field of vision neuroscience, but also about life in general. For this you have my

sincerest gratitude. Your support, intelligence and work ethic towards the timely completion of

my degree (and in general) has been outstanding, and I could not have asked for a better

supervisor to help me throughout the last two years. You have truly inspired me by showing me

that hard work really does pay off and if you put your best foot forward, anything can be

accomplished.

The next person I have to say a large thank you to is Dr. Herbert Goltz, my co-supervisor on this

project. Without your extensive knowledge, support and help throughout this entire process, this

thesis would not have been possible. Each time the project hit some sort of speed bump, it was

your innovative ideas that helped get the ball rolling again. Thank you so much for all of your

help and support.

Next, I would like to thank my program advisory committee, Dr. Susanne Ferber and Dr. Luc

Tremblay for their contribution into this thesis and making it what it is. If it were not for your

input on every section of this project, it would not have gone as smoothly as it did. You both

helped me to think more like a scientist, and thus helped me to begin to tap into my true research

potential. For that I am extremely grateful.

The technical side of this project was a daunting and extremely involved task. It would not have

been possible to run this project without the help of Luke Gane. Thank you so much for

everything you have done. It really would have been impossible without you. Additionally, I

have to extend a large thank you to Mano Chandrakumar. When I started in the lab, you took me

under your wing and helped pick this project off the ground. Thank you for always being there.

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To the other members of the Eye Movement and Vision Neuroscience Laboratory, thank you so

much! It has honestly been the most memorable two years of my life. Working as a team to

create and produce sound science has taught me so much. Each one of you have changed me in

some way for the better and I am forever grateful.

I would specifically like to thank our orthoptist, Linda Colpa who recruited and screened each

participant involved in this study. Without your dedication this project would have literally been

impossible to run. I am forever indebted to you for helping me throughout this entire process. It

has been an honor working with such an amazing person.

Additionally, this project would have been impossible to pursue without the generous funding of

the Vision Science Research Program scholarship (VSRP), a joint University Health Network,

University of Toronto venture.

Last, but certainly not least, I would like to thank all of my family and friends for supporting me

over the last two years. You have kept me grounded and encouraged me to chase after my

dreams and I am extremely grateful for that. Of course, a special mention has to go to my

parents, Lisa and Lawrence and my brother Michael for their unconditional support.

This is for all of you who have made this thesis a reality. Thank you again.

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Table of Contents

ACKNOWLEDGMENTS .......................................................................................................................... III

TABLE OF CONTENTS ............................................................................................................................ V

LIST OF TABLES ................................................................................................................................. VIII

LIST OF FIGURES .................................................................................................................................. IX

LIST OF ABBREVIATIONS ...................................................................................................................... XI

CHAPTER 1 INTRODUCTION .................................................................................................................. 1

1.1 GENERAL INTRODUCTION......................................................................................................... 1

1.2 TRANSDUCTION OF VISUAL INFORMATION AND VISUAL DEVELOPMENT .................................. 3

1.2.1 Visual transduction ............................................................................................................ 3

1.2.2 Visual development ............................................................................................................ 6

1.3 AMBLYOPIA ........................................................................................................................... 8

1.3.1 Neural correlates of the amblyopic deficits...................................................................... 8

1.3.2 Classifications ................................................................................................................. 10

1.3.3 Deficits............................................................................................................................. 11

1.3.5 Differences among the amblyopic subtypes .................................................................... 16

1.3.5 Treatment......................................................................................................................... 17

1.3.6 Summary .......................................................................................................................... 18

1.4 VISUALLY-GUIDED REACHING .............................................................................................. 19

1.5 PRISM ADAPTATION ............................................................................................................. 21

1.5.1 Observations during prism adaptation ............................................................................ 21

1.5.2 The prism adaptation paradigm ...................................................................................... 22

1.5.3 What drives sensorimotor adaptation ............................................................................. 23

1.5.4 Adaptive processes during prism adaptation ................................................................. 26

1.5.5 Neural correlates of prism adaptation ............................................................................ 33

1.5.6 Prism adaptation in [other] pathological conditions ..................................................... 36

1.5.7 Summary .......................................................................................................................... 40

CHAPTER 2 HYPOTHESES AND OBJECTIVES ....................................................................................... 41

2.1 HYPOTHESES ........................................................................................................................ 41

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CHAPTER 3 MATERIALS AND METHODS ............................................................................................. 43

3.1 MATERIALS AND METHODS................................................................................................... 43

3.1.2 Apparatus ........................................................................................................................ 44

3.2 PROCEDURE.......................................................................................................................... 47

3.2.1 Pointing with feedback (prism adaptation baseline) ...................................................... 47

3.2.2 Open-loop pointing (total shift task) .............................................................................. 48

3.2.3 Visual straight ahead (visual shift task) ......................................................................... 49

3.2.4 Blind straight ahead pointing (proprioceptive shift task) .............................................. 51

3.2.5 Prism adaptation ............................................................................................................ 52

3.2.6 Prism de-adaptation ....................................................................................................... 53

3.3 DATA ANALYSIS ................................................................................................................... 54

3.3.1 Primary outcome measures ............................................................................................. 55

CHAPTER 4 RESULTS ........................................................................................................................... 60

4.1 POINTING WITH FEEDBACK (PRISM ADAPTATION BASELINE).................................................. 60

4.2 PRISM ADAPTATION TASK ..................................................................................................... 61

4.2.1 Spatial properties ............................................................................................................ 61

4.2.2 Temporal properties ........................................................................................................ 65

4.3 PRISM DE-ADAPTATION TASK ............................................................................................... 75



4.4 COMPARISON OF BASELINE, PRISM ADAPTATION AND DE-ADAPTATION ................................. 83

4.4.1 Comparison of movement duration across the three blocks ........................................... 83

4.4.2 Comparison of the magnitude of adaptation and de-adaptation ..................................... 84

4.5 REALIGNMENT TASKS ........................................................................................................... 85

4.5.1 Wilkinson's additivity model ............................................................................................ 88

CHAPTER 5 DISCUSSION AND FUTURE DIRECTIONS ........................................................................... 90

5.1 JUSTIFICATION OF THE EXPERIMENTAL PARADIGM ................................................................ 90

5.2 THE PRISM ADAPTATION TASK .............................................................................................. 92



5.3 COMPARISON OF PRISM ADAPTATION AND DE-ADAPTATION .................................................. 99

5.4 SPATIAL REALIGNMENT AND WILKINSON'S ADDIVITY MODEL ........................................... 100

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5.4.1 Considerations for Additivity in visually-normal controls ............................................ 100

5.4.2 Considerations for Addivity in anisometropic amblyopia ............................................. 101

5.5 ARE THE FINDINGS DUE TO MORE THAN JUST VISUAL ACUITY? ............................................ 103

5.6 INSIGHT INTO THE PRISM ADAPTATION PARADIGM .............................................................. 104

5.7 IMPORTANCE OF THIS STUDY .............................................................................................. 105

5.8 CONCLUSION ...................................................................................................................... 106

5.9 FUTURE DIRECTIONS ........................................................................................................... 106

5.9.1 Pointing kinematics during baseline, adaptation & de-adaptation .............................. 106

5.9.2 Visual-haptic integration in amblyopia ......................................................................... 109

5.10 LIMITATIONS ...................................................................................................................... 110

REFERENCES ...................................................................................................................................... 111

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List of Tables

TABLE 3-1: CLINICAL DATA FOR ALL PARTICIPANTS.............................................................................. 44

TABLE 4-1: TIME CONSTANT AND R2 VALUES DURING PRISM ADAPTATION. ........................................... 70

TABLE 4-2: TIME CONSTANT AND R2 VALUES DURING PRISM DE-ADAPTATION. ..................................... 80

TABLE 4-3: "SHIFTS" FOR ALL OF THE PARTICIPANTS INCLUDED IN THIS STUDY. .................................... 87

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List of Figures

FIGURE 1-1: EXAMPLE OF THE PRISM ADAPTATION PARADIGM ................................................................ 2

FIGURE 1-2: TRANSMISSION OF INFORMATION TO THE VISUAL CORTEX FROM THE EXTERNAL WORLD ..... 4

FIGURE 1-3: RAY DIAGRAM THROUGH A WEDGE PRISM. ........................................................................ 22

FIGURE 1-4: EXPECTED SHIFT IN THE EYE-HEAD REFERENCE FRAME...................................................... 28

FIGURE 1-5: EXPECTED SHIFT IN THE HAND-HEAD REFERENCE .............................................................. 29

FIGURE 1-6: EXPECTED SHIFT IN THE HAND-EYE REFERENCE ................................................................. 30

FIGURE 3-1: EXPERIMENTAL SETUP ON THE VIRTUAL SURFACE APPARATUS (VSA). .............................. 46

FIGURE 3-2: FLOW CHART OF THE PROCEDURE FOR ALL PARTICIPANTS. ................................................ 47

FIGURE 3-3: POINTING WITH FEEDBACK (PRISM ADAPTATION BASELINE) TASK...................................... 48

FIGURE 3-4: OPEN LOOP POINTING (TOTAL SHIFT TASK) ........................................................................ 49

FIGURE 3-5: VISUAL STRAIGHT AHEAD (VISUAL SHIFT TASK) ................................................................ 50

FIGURE 3-6: STRAIGHT AHEAD BLIND POINTING (PROPRIOCEPTIVE SHIFT TASK) .................................... 51

FIGURE 3-7: PRISM ADAPTATION TASK DURING EARLY (A) AND LATE (B) TRIALS ................................. 52

FIGURE 3-8: PRISM DE-ADAPTATION TASK DURING EARLY (A) AND LATE (B) TRIALS. ........................... 53

FIGURE 3-9: SAMPLE CALCULATION FOR NORMALIZED MAGNITUDE OF ADAPTATION ............................ 56

FIGURE 3-10: REPRESENTATIVE DATA FOR ONE VISUALLY-NORMAL CONTROL DEPICTING THE

EXPONENTIAL FIT ANALYSIS (A) AND BINNING ANALYSIS (B) ....................................................... 57

FIGURE 3-11: SAMPLE CALCULATION FOR THE "SHIFTS" IN REFERENCE FRAMES ................................... 58

FIGURE 4-1: GROUP MEAN ACCURACY (A) AND PRECISION (B) FOR THE BASELINE BLOCK. ................... 60

FIGURE 4-2: COMPARISON OF POINTING ACCURACY (A) AND POINTING PRECISION (B) TO DIFFERENT

TARGET POSITIONS ........................................................................................................................ 61

FIGURE 4-3: MEAN NORMALIZED MAGNITUDE OF ADAPTATION ............................................................. 62

FIGURE 4-4: COMPARISON OF THE INITIAL POINTING ERROR .................................................................. 63

FIGURE 4-5: RESULTS OF THE ANALYSIS PERFORMED ON POINTING ACCURACY TO DIFFERENT TARGET

POSITIONS. .................................................................................................................................... 64

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FIGURE 4-6: PRECISION OF MOVEMENTS TO THE VARIOUS TARGET POSITIONS. ...................................... 65

FIGURE 4-7: EXPONENTIAL FITS FOR 11 VISUALLY-NORMAL CONTROLS (BLUE) AND SEVEN PEOPLE WITH

ANISOMETROPIC AMBLYOPIA (RED). .............................................................................................. 68

FIGURE 4-8: COMPARISON OF MEAN TIME CONSTANT VALUES. .............................................................. 69

FIGURE 4-9: AVERAGED BINNED DATA FOR ADAPTATION FOR ALL PARTICIPANTS ACROSS THE TWO

EXPERIMENTAL GROUPS. ............................................................................................................... 71

FIGURE 4-10: SAMPLE CALCULATION OF MEAN RESIDUAL DISTANCE FROM LINEAR FUNCTION .............. 72

FIGURE 4-11: COMPARISON OF THE MEAN OF RESIDUALS AT THE BEGINNING (TIME CONSTANT + 2) AND

END OF ADAPTATION (LAST 10 TRIALS OF THE ADAPTATION BLOCK). ............................................ 73

FIGURE 4-12: RELATION BETWEEN TIME CONSTANT AND MEAN OF RESIDUALS. .................................... 74

FIGURE 4-13: COMPARISON OF NORMALIZED MAGNITUDE OF DE-ADAPTATION BETWEEN THE TWO

GROUPS. ........................................................................................................................................ 75

FIGURE 4-14: POINTING ACCURACY (A) AND PRECISION (B) TO EACH TARGET POSITION DURING THE

PRISM ADAPTATION TASK .............................................................................................................. 76

FIGURE 4-15: EXPONENTIAL DECAY FUNCTIONS FOR THE DE-ADAPTATION BLOCK FOR VISUALLY-

NORMAL CONTROLS (BLUE) AND PEOPLE WITH ANISOMETROPIC AMBLYOPIA (RED). ...................... 79

FIGURE 4-16: TIME CONSTANT COMPARISON FOR THE DE-ADAPTATION BLOCK. .................................... 81

FIGURE 4-17: GLOBAL EXPONENTIAL DECAY FITS FOR THE CONTROL (BLUE) AND AMBLYOPIA (RED)

GROUPS DURING PRISM DE-ADAPTATION. ...................................................................................... 81

FIGURE 4-18: AVERAGED BINNED DATA FOR DE-ADAPTATION FOR ALL PARTICIPANTS ACROSS THE TWO

EXPERIMENTAL GROUPS. ............................................................................................................... 82

FIGURE 4-19: COMPARISON OF THE NORMALIZED MAGNITUDE OF ADAPTATION AND DE-ADAPTATION

POOLED ACROSS GROUPS. .............................................................................................................. 84

FIGURE 4-20: RELATION BETWEEN NORMALIZED MAGNITUDE OF ADAPTATION AND DE-ADAPTATION ... 85

FIGURE 4-21: TOTAL, VISUAL AND PROPRIOCEPTIVE SHIFT POOLED ACROSS THE TWO GROUPS. ............. 86

FIGURE 4-22: COMPARISON OF REALIGNMENT AFTEREFFECTS BETWEEN LITERATURE, CONTROL AND

ANISOMETROPIC AMBLYOPIA VALUES. .......................................................................................... 88

FIGURE 4-23: COMPARISON OF THE |TOTAL| SHIFT WITH THE |SUMMED| SHIFTS. ..................................... 89

xi

List of Abbreviations

3D Three-dimensional

AD Alzheimer's Disease

aIPS Anterior Intraparietal Sulcus

CNS Central Nervous System

CRT Cathode Ray Tube

D Diopter

FSR Force Sensitive Resistor

HD Huntington's Disease

IPS Intraparietal Sulcus

LGN Lateral Geniculate Nucleus of the Thalamus

MD Monocular Deprivation

mIPS Medial Intraparietal Sulcus

MLE Maximum Likelihood Estimation

mOPJ Medial Occipital-Parietal Junction

MRI Magnetic Resonance Imaging

ND Neutral Density

OC Optotrak Certus

OD Ocular Dominance

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PD Parkinson's Disease

PET Positron Emission Tomography

POS Parietal Occipital Sulcus

PPC Posterior Parietal Cortex

PRR Parietal Reach Region

SPL Superior Parietal Lobule

STG Superior Temporal Gyrus

STS Superior Temporal Sulci

V1 Primary Visual Cortex

VA Visual Acuity

VSA Virtual Surface Apparatus

1

Chapter 1 Introduction

1.1 General Introduction

Amblyopia, or "lazy-eye", is a neurodevelopmental disorder of vision that is characterized by a

unilateral (albeit sometimes bilateral) reduction in best-corrected visual acuity (Holmes &

Clarke, 2006). The deficits associated with amblyopia are not the result of a structural pathology

of the eye itself (von Noorden, 1977), rather the locus of amblyopia is the primary visual cortex

(V1; Algaze, Roberts, Leguire, Schalbrock, & Rogers, 2002) and may extend to the extra-striate

visual pathways (Barnes, Hess, Dumoulin, Achtman, & Pike, 2001).

The extensive range deficits within the visual system have been and continue to be an area of

interest for researchers studying amblyopia. However, there has been recent evidence to suggest

that visuomotor function may also be impaired. For example, it has been demonstrated that

people with amblyopia display deficits in movement initiation and execution during prehension

(Grant, Melmoth, Morgan, & Finlay, 2007; Suttle, Melmoth, Finlay, Sloper, & Grant, 2011), and

have adopted alternate kinematic strategies, namely an increased acceleration phase and

decreased peak velocity/acceleration during a single motor action, to attain a similar precision

and accuracy to visually-normal controls on a simple pointing task during binocular viewing

(Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, Crawford, et al., 2011).

All actions, whether in the oculomotor or manual motor domain, are under adaptive control

where behaviour of the muscles are modified in response to small perturbations in the external

sensory environment. In this way, accurate motor actions to intended visual targets are

accomplished (Crawford, Medendorp, & Marotta, 2004). The visual system is often the most

reliable (least variable) sense and is therefore often used to coordinate and initiate these adaptive

changes in motor output (Ernst & Bülthoff, 2004; Goodale, 2011). Interestingly, Raashid, Wong,

Chandrakumar, Blakeman, and Goltz (2013) have demonstrated that visuomotor adaptation is

impaired in the oculomotor system in amblyopia as evidence by a decreased ability to adapt

saccadic eye movements to an external perturbation in the visual environment.

2

The purpose of this thesis was to examine adaptation of the manual motor system in amblyopia

to a change in the external visual environment. This way a conclusion can be made about

visuomotor adaptation in amblyopia in general - is saccadic adaptation a special case or do these

deficits extend to the manual motor system as well? The experimental manipulation used to

assess the adaptive ability of the manual motor system in amblyopia to a change in the external

visual environment was prism adaptation.

Modification of limb trajectory in response to optically displacing wedge prisms is a well-

established method of sensorimotor adaptation. Prism adaptation involves adapting the motor

system to a novel spatial environment via a shift of the visual world (Fernandez-Ruiz & Diaz,

1999). For instance, when a subject points to a visual target while wearing wedge prisms that

displace the optical world to the left, an initial pointing error to the left of the target occurs. In

the presence of visual feedback and with repeated target pointing, this error decreases rapidly,

eventually reaching a plateau phase with a similar precision and accuracy to baseline pointing

(Figure 1-1). If the same task is repeated after the prisms are removed, a pointing error in the

opposite direction occurs, in this case to the right, before the error decreases again (Fernandez-

Ruiz & Diaz, 1999; Figure 1-1).

Figure 1-1: Example of the prism adaptation paradigm showing the baseline, adaptation and de-adaptation blocks in response to left-shifting prisms.

3

1.2 Transduction of visual information and visual development

1.2.1 Visual transduction

Transduction and processing of visual stimuli from the external world begins when light strikes

the retina located at the back of the eye. Depending on the type of visual information used to

initiate signal transduction, the retina relays information to the retinal ganglion cells which

project to four distinct structures: 1) the suprachiasmatic nucleus which controls diurnal rhythms

(Sollars et al., 2003); 2) the pretectum which modulates pupillary response (Purves, 2012a); 3)

the superior colliculus for the control of eye movements (Daw, 2006); and 4) the lateral

geniculate nucleus of the thalamus (LGN) which projects to the striate cortex and is involved in

mediating vision and visual perception (Fujita et al., 2001). The pathway involved in relaying

information to the LGN is referred to as the retinogeniculostriate, or more simply, primary visual

pathway (Daw, 2006; Purves, 2012a).

1.2.1.1 The retinogeniculostriate visual pathway

The information from retinal ganglion cells is first past to the lateral geniculate nucleus of the

thalamus (LGN) during transduction through the primary visual pathway. The LGN is commonly

referred to as a "relay" component of the primary visual pathway, however it has been shown

that the LGN is actually involved in processing of the visual signal (Derrington, 2001; Piscopo,

El-Danaf, Huberman, & Niell, 2013; Seim, Valberg, & Lee, 2012). The LGN then transmits the

processed visual signal to V1, or striate, cortex (Daw, 2006).

Information entering V1 from the LGN enters at layer 4c. This is the thickest layer of the six

comprising the primary visual cortex. At layer 4c, the information is still separate coming from

the left and right eye so that the input is monocular to this layer. As the input from the two eyes

is relayed to layers above and below 4c, these signals integrate and require binocular input, or

information from the two eyes together, to function and develop properly (Purves, 2012a).

4

Figure 1-2: Transmission of information to the visual cortex from the external world (Goodale &

Westwood, 2004)1.

Proper binocular integration depends on the ability to stimulate both eyes equally soon after birth

(Braddick et al., 1980; Hubel & Wiesel, 1965; Scholl, Tan, & Priebe, 2013; Smith &

Trachtenberg, 2007), with each eye competing for representation in V1 (Gordon & Stryker,

1996; Hubel, Wiesel, & LeVay, 1977; Wiesel & Hubel, 1965). Cells that receive input from

both eyes uniformly develop to respond preferentially to binocular stimulation through Hebbian

synaptic learning rules where those that fire together, wire together (Constantine-Paton, Cline, &

Debski, 1990). These cells are referred to as the "binocular" cells of V1. In contrast, cells that

receive more input from one eye or the other develop to respond preferentially to monocular

stimulation from that eye, and are thusly named "monocular". Interestingly, the distribution of

monocularly and binocularly stimulated cells follows a stereotypical band pattern in visually-

normal individuals, where the information for each eye monocularly is demarcated by narrow

bands of binocular "cross-over" areas (Hubel et al., 1977). The concept of this pattern of ocular

1 Reprinted from Current Opinion in Neurobiology, 14/2, Goodale MA & Westwood DA, An evolving view of

duplex vision: separate but interacting cortical pathways for perception and action, 203-211., Copyright (2004), with

permission from Elsevier. Reprinted from The Lancet, 2, Goodale MA & Westwood DA, An evolving view of

duplex vision: separate but interacting cortical pathways for perception and action, 203-211., Copyright (2004), with

permission from Elsevier.

5

dominance (OD) in the primary visually cortex was initially coined by Hubel and Wiesel in their

Nobel prize winning work beginning in the early 1960's.

Approximately 70% of the neurons projecting to the striate cortex will innervate binocularly

driven cells (Wright, 2006). When binocularity is affected by some pathology (see section 1.3

Amblyopia: the result of abnormal visual input in early life), the cross-over areas within the OD

columns of V1 essentially disappear (Horton & Hocking, 1996; Horton, Hocking, & Kiorpes,

1997). This leads to a loss of stereopsis and optimal binocular vision (Hubel et al., 1977).

Therefore, proper binocular stimulation soon after birth is required for typical visual

development (Wright, 2006). The period of time in which the development of the visual system

occurs is termed the visual critical period (Hubel & Wiesel, 1962; see section 1.2.2.2 The visual

critical period).

Once information leaves the visual cortex, additional processing of the signal occurs along the

extra-striate visual association areas at the temporal (ventral stream) and parietal (dorsal stream)

lobes (Hebart & Hesselmann, 2012). The ventral visual stream, or "vision-for-perception"

pathway, extends from the occipital to the temporal lobe. It is thought to be mainly involved in

the recognition and discrimination of object shape and form in space (Hebart & Hesselmann,

2012). The dorsal visual stream, or "vision-for-action" pathway, extends from the occipital to the

parietal lobe. This processing area is primarily involved in both the perceptual detection of

motion (Atkinson et al., 2006) and visually-guided movements including grasping (Culham et

al., 2003; Hebart & Hesselmann, 2012) and prehension (Goodale, 2011) through recognition of

object position in space (Goodale & Milner, 1992).

6

1.2.2 Visual development

1.2.2.1 Critical periods of development

A critical period is generally defined as an period of increased neuronal plasticity where

exposure to different stimuli can result in major and often permanent rewiring of the brains

neural connections (Katz, 1999). Critical periods have now been identified for several sensory

systems including audition (Niparko et al., 2010), vision (Blakemore & Cooper, 1970; Hubel &

Wiesel, 1965; Hubel et al., 1977; LeVay, Wiesel, & Hubel, 1980; Sengpiel, Stawinski, &

Bonhoeffer, 1999; Wiesel & Hubel, 1963b), olfaction (Tsai & Barnea, 2014) the vestibular

system (Eugene, Deforges, Vibert, & Vidal, 2009) and somatosensation (see review by O'Leary,

Ruff and Dyke, 1994). Critical periods have also been identified for the motor system (Friel,

Chakrabarty, Kuo, & Martin, 2012; Soiza-Reilly, Fossati, Ibarra, & Azcurra, 2004; Walton,

Lieberman, Llinas, Begin, & Llinas, 1992) and other cognitive functions such as memory (Deng,

Saxe, Gallina, & Gage, 2009; Shors et al., 2001; Snyder, Hong, McDonald, & Wojtowicz, 2005),

and language acquisition (Hakuta, Bialystok, & Wiley, 2003; Johnson & Newport, 1989; Snow

& Hoefnagel-Hohle, 1978).

1.2.2.2 The visual critical period

During the critical period, the visual cortex is highly modifiable and depends on the influence of

normal visual stimuli in order to develop properly (Katz, 1999; Wright, 2006). Wiesel and

Hubel (1963a) showed that when kittens are reared with one eye sutured shut, or in a

monocularly deprived (MD) state, there is a dramatic modification of the function and

histological morphology of OD columns in the primary visual cortex. The sutured eye is not

privy to external visual stimuli, thus decreasing its representation in the "binocular" and

"monocular" columns of V1. MD ultimately results in an over representation of the non-sutured,

or fellow eye in the striate cortex (Hubel et al., 1977).

Some of the noted deficits associated with abnormal visual stimulation during early post-natal

life, for example by MD, include decreased contrast sensitivity, loss of stereopsis or three-

dimensional (3D) vision and decreased visual acuity in the deprived eye (McKee, Levi, &

7

Movshon, 2003). Each one has a slightly different, yet overlapping critical period of

development.

Visual acuity (VA) improves rapidly after birth and continues to develop throughout adolescence

to about age 15 (Leat, Yadav, & Irving, 2009); however, it is comparable to adults by age three

when single letters are used to assess thresholds (Heron, Dholakia, Collins, & McLaughlan,

1985). Similarly, stereopsis improves over time, beginning at about 3-4 months of age and

develops rapidly to about age 15 (Heron et al., 1985).

Although controversial, it is generally agreed that the visual critical period occurs between 1

week and 3 months of age (Wright, 2006). This said, it has been demonstrated that children are

susceptible to abnormal visual development up through the age of seven or older, an epoch

termed the sensitive period of visual development (Keech & Kutschke, 1995; Lewis & Maurer,

2005). It is therefore imperative that there is normal visual stimulation during the critical and

sensitive periods of development to ensure proper formation of the visual transduction pathways.

8

1.3 Amblyopia

Amblyopia is a disorder of vision that results as a consequence of decreased afferent input to the

visual cortex during the critical/sensitive period of visual development. Amblyopia refers to

anomalous maturation of the visual system that occurs secondary to atypical visual stimulation

during early childhood (Wright, 2006). The development of amblyopia is most often associated

with the presence of anisometropia, defined as an interocular difference in refractive error, or

more simply a difference in the ability of the two eyes to focus light on the retina (Gupta, 2008),

strabismus (eye-misalignment), or a mixed mechanism of the two (Wright, 2006). On rare

occasion, amblyopia can also result as a consequence of deprivation due to significant opacity of

the cornea which prevents light from stimulating the retina in early life (e.g. congenital cataract)

or due to physical occlusion of the eye by way of a congenital ptosis (drooping eyelid; Wong,

2014).

Amblyopia is a neurodevelopmental disorder that has no causal pathology associated with the

eye itself (von Noorden, 1977) as such, optical correction cannot alleviate its associated

symptoms. This means that prescription glasses alone cannot result in the recovery of normal

vision in people with amblyopia. Rather, the spatial and temporal visual deficits associated with

amblyopia result as a consequence of dysfunction in visual processing as early as V1 (Barnes et

al., 2001) extending up through the extra-striate visual areas, located along the parietal and

temporal lobes (Simmers, Ledgeway, Mansouri, Hutchinson, & Hess, 2006).

1.3.1 Neural correlates of the amblyopic deficits

The first indication that the locus of the amblyopic deficit begins at the level of V1 was

established by obtaining single cells recordings of the cat striate cortex after early childhood

MD. Using electrophysiological analysis, Wiesel and Hubel (1963b) found that the majority of

cells in the primary visual cortex in kittens reared in a MD state were driven by the fellow eye, or

non-monocularly deprived eye, whereas ~1% of cells recorded were stimulated in response to

amblyopic eye viewing of their patterned visual stimulus.

Additionally, Wiesel and Hubel (1963a) discovered that LGN appeared deprived upon

histological examination of the kitten reared in a MD state. It was hypothesized that this was due

9

to retrograde atrophy resulting from decreased input to the LGN from the primary visual cortex

(Wiesel & Hubel, 1963a). Cells within the LGN compete for synaptic connections in V1 during

development, and the strength of these connections depends on normal visual stimulation during

early childhood. When MD is present soon after birth, the binocular competition for regular

geniculate growth is disrupted, resulting in an atrophied appearance of the LGN in the layers

supplied by the deprived eye (Guillery, 1972). This same result of abnormal histological

morphology of the LGN in amblyopia has been demonstrated in both the monkey (von Noorden,

1973; von Noorden & Middleditch, 1975) and human (von Noorden & Crawford, 1992) model

of the disease, where cell shrinkage is observable in the layers of the LGN supplied by the

amblyopic eye.

In addition to the single cell recordings, additional evidence for a cortical locus involved in

amblyopia has been demonstrated via high resolution imaging studies.

Functional magnetic resonance imaging systems allow for high-resolution images of both

shallow and deep brain structures to be obtained with relative ease, and therefore have been used

extensively to study amblyopia in living human beings. One of the main findings of these

studies demonstrates that cortical cells of V1 are preferentially affected by abnormal visual

stimulation in early life, and this deficit is most prominent during amblyopic eye viewing

(Algaze et al., 2002; Barnes et al., 2001; Choi et al., 2001). Additionally, fMRI has provided the

first evidence of a shift in OD towards the fellow eye during normal visual stimulation the

human model of amblyopia (Goodyear, Nicolle, & Menon, 2002).

Based on psychophysical data such as the contrast sensitivity deficit (Nordmann, Freeman, &

Casanova, 1992), spatial and temporal crowding (Bonneh, Sagi, & Polat, 2007), and abnormal

detection of motion defined form (Giaschi, Regan, Kraft, & Hong, 1992), it was hypothesized

that the extra striate visual processing areas are also affected in amblyopia (Kiorpes, Kiper,

O'Keefe, Cavanaugh, & Movshon, 1998). Functional imaging has been able substantiate this

idea by demonstrating that there is generalized decreased activation of areas V1-V5 as well as at

the ventral and dorsal visual streams (Barnes et al., 2001; Li, Dumoulin, Mansouri, & Hess,

2007). More specifically, decreased glucose metabolism at the inferior temporal (ventral

stream) and superior parietal (dorsal stream) lobules has been demonstrated by positron emission

tomography (PET) in amblyopia (Choi et al., 2002).

10

1.3.2 Classifications

Amblyopia is typically segregated into four main classifications based on its associated etiology:

deprivation, anisometropia, strabismus, and a mix of both strabismus and anisometropia (McKee

et al., 2003). These etiologies have been demonstrated to cause amblyopia, but have also been

shown to come about as a consequence of amblyopia (Birch & Swanson, 2000; Kiorpes &

Wallman, 1995; Lepard, 1975). This indicates that there a complex relationship between

amblyopia and strabismus/anisometropia/deprivation in early childhood (McKee et al., 2003).

1.3.2.1 Anisometropic amblyopia

Anisometropia is defined as a difference in refractive power between the two eyes (The Pediatric

Eye Disease Investigator Group, 2006). If anisometropia is present during the sensitive period of

visual development, it is possible that it may be associated with amblyopia. If amblyopia does

occur, it will do so as a result of monocular suppression of one eye, typically the more hyperopic

eye (Wright, 2006) to prevent conflicting binocular information from reaching the visual cortex

(Wensveen, Harwerth, & Smith, 2001). Anisometropic amblyopia is harder to diagnose as there

is generally no observable oculomotor deficit such as an eye-misalignment associated with it

(Wright, 2006).

1.3.2.2 Strabismic amblyopia

Strabismus refers to misalignment of the two eyes. One eye can be turned nasally (esotropia),

temporally (exotropia), upwards (hypertropia) or downwards (hypotropia) while the other fixates

centrally (Granet & Khayali, 2011). The chance of developing amblyopia as a result of early

childhood strabismus increases if one eye is preferentially affected, rather than alternating

fixation between the two eyes (Sireteanu, 1982). Clinical suppression of one eye is involved in

the development of amblyopia as a result of early childhood strabismus. In this case, cortical

suppression is useful to prevent diplopia, or double vision, that would result in confusion if the

two images reached and were processed by the primary visual cortex concurrently (Wong, 2011).

11

1.3.2.3 Deprivational amblyopia

Deprivational amblyopia occurs as a result of partial or complete occlusion of one eye during

early childhood (Mansouri, Stacy, Kruger, & Cestari, 2013). The most common causes of

deprivational amblyopia are congenital cataract (Mansouri et al., 2013), resulting in increased

opacity of the cornea and thus decreased interaction with the visual environment (Wong, 2014)

and congenital ptosis (drooping eye lid; Griepentrog, Diehl, & Mohney, 2013; Wong, 2014). It

is the least common form of the disease, especially in the Western world as the occlusion is

typically addressed early in life; however, it results in the deepest form of amblyopia when

present (Simon & Kaw, 2001).

1.3.3 Deficits

There is a vast range of deficits associated with amblyopia, in both perceptual and motor

responses to visual stimuli. These will be discussed in detail below.

1.3.3.1 Deficits in spatial vision

Spatial vision refers the ability of the visual system to integrate and assess visual stimuli in

spatially distinct locations. In other words it refers to the ability of the visual system to locate and

detect an object's position in space (De Valois & De Valois, 2002).

Decreased visual acuity in amblyopia has been well documented throughout the literature for a

number of decades. Decreased optotype acuity, or the ability to discern optotypes (commonly

letters) of different sizes in the amblyopic, but not fellow eye is generally the first indicator that

amblyopia may be present (Bonneh, Sagi, & Polat, 2004; McKee et al., 2003; The Pediatric Eye

Disease Investigator Group, 2002). Additionally, deficits in Vernier/hyperacuity, as measured

by the threshold required to detect a horizontal offset between two vertical lines placed above

one another, have been identified in amblyopia. (Bradley & Freeman, 1985; Kiorpes, Kiper, &

Movshon, 1993; Levi & Klein, 1982a, 1985). Lastly, grating acuity, or the ability to discern the

direction of alternating black and white stripes of varying spatial frequencies has been

demonstrated to be affected by amblyopia (Kiorpes et al., 1993; Levi & Klein, 1985; McKee et

al., 2003).

12

People with amblyopia exhibit decreased contrast sensitivity in the affected eye, most often for

high spatial frequency stimuli (Abrahamsson & Sjostrand, 1988; Levi & Harwerth, 1977), but

this deficit has also been shown in strabismic amblyopia for low spatial frequency gratings (Hess

& Howell, 1977). More subtle contrast sensitivity deficits have also been established for the

fellow eye, indicating that the eye with normal visual acuity displays amblyopic deficits that

differ significantly from visually-normal controls (Leguire, Rogers, & Bremer, 1990).

Amblyopia has long been associated with a loss of stereopsis, or 3D vision, due to a lack of

response from improperly stimulated binocular cells during the critical period of development

(Hubel & Wiesel, 1962). It has been shown that people with amblyopia have decreased (Wallace

et al., 2011) or absent (Birch, 2013) stereo-vision. This said, there is a definite spectrum of

binocular visual loss with its presentation strongly related to the etiology of the deficit (Wright,

2006; see section 1.3.5 Differences among the amblyopic subtypes).

With respect ventral visual stream processing, people with amblyopia exhibit impaired global

processing of orientation (Husk & Hess, 2013), global shape detection (Dallala, Wang, & Hess,

2010; Hess, Wang, Demanins, Wilkinson, & Wilson, 1999), global shape discrimination (Jeffrey,

Wang, & Birch, 2004), spatial localization in both the affected (Fronius, Sireteanu, & Zubcov,

2004; Hess & Holliday, 1992) and fellow eye (Levi & Klein, 1985), global contour processing

(Levi, Yu, Kuai, & Rislove, 2007) and spatial crowding (Bonneh et al., 2004, 2007; Levi &

Klein, 1985). Additionally, along the dorsal stream, people with amblyopia display perceptual

deficits in the detection of global motion (Ho et al., 2005; Simmers, Ledgeway, Hess, &

McGraw, 2003; Simmers et al., 2006). It is unclear whether these deficits result as a

consequence of amplification of the decreased signal from V1 or if there is actual dysfunction at

these cortical loci (Kiorpes, 2006; Levi, 2006).

1.3.3.2 Deficits in temporal vision

In addition to the issues in spatial vision outlined above, deficits in temporal vision have also

been identified for people with amblyopia. Temporal vision refers to the ability of the visual

system to process and integrate visual stimuli over time (Bonneh et al., 2007).

It has been shown previously that people with amblyopia exhibit dysfunction on temporal

integration tasks (Altmann & Singer, 1986; Huang, Li, Deng, Yu, & Hess, 2012). For example,

13

Huang et al. (2012) demonstrated that people with amblyopia display temporal synchrony

deficits. This was observed via decreased sensitivity of both the amblyopic and fellow eyes to

discrimination of visual stimuli presented 180° out of phase compared to three other synchronous

flashing dots.

Temporal crowding has also been identified as a perceptual deficit in people with amblyopia.

Bonneh et al. (2007) showed that rapidly presented simple visual stimuli (large black digits on a

grey background) affected visual acuity in strabismic amblyopia significantly more than in

anisometropic amblyopia or visually-normal controls.

Other temporal visual deficits in amblyopia have been identified for temporal contrast sensitivity

tasks in both strabismic and anisometropic amblyopia (Ellemberg, Lewis, Maurer, & Brent,

2000; Wesson & Loop, 1982), an inability to detect motion-defined form in both the affected

(Hayward, Truong, Partanen, & Giaschi, 2010) and fellow eyes (Giaschi et al., 1992; Hayward

et al., 2010), as well as increased latency in response to visual stimuli. This has been

demonstrated by increased reaction time when completing a perceptual task, especially during

amblyopic eye viewing (Hamasaki & Flynn, 1981; Loshin & Levi, 1983), increased saccadic

latencies (Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, & Wong, 2010; Raashid et al., 2013)

and increased neuronal latencies of visual evoked potentials (Davis et al., 2003).

1.3.3.3 Motor deficits

Although a wide range of perceptual deficits are still being investigated, recently there has been

some interest in elucidating the effect of abnormal vision in amblyopia on motor control.

Generally, vision is used to calibrate the motor system to allow for accurate movements to an

intended target. Vision is required to encode the position and location of the object via the

ventral visual stream. Vision is then responsible for online correction of the movement by way

of the dorsal visual stream (Goodale, 2011). If this visual sensory signal is abnormal, presumably

the motor output and the online correction of the movement would be affected. Grant et al.

(2007) found this by showing that adult participants with amblyopia exhibit subtle deficits in

movement planning and more pronounced issues with movement execution. More specifically,

they showed that people with amblyopia display significantly longer movement execution times

as well as more errors upon reaching the target. Subsequently, Suttle et al. (2011) found that

14

independent of the etiology of amblyopia, children who suffer from this disorder required much

more time to execute the intended movement, made many more errors upon reaching the target

and did not show a binocular advantage, where viewing binocularly improves precision and

accuracy of motor action than viewing monocularly, as was seen in age-matched visually-

normal controls. These studies provided evidence that there is visuomotor dysfunction in

amblyopia; however the extent to which it affects these participants in the real world has yet to

be determined.

Niechwiej-Szwedo et al. (2010) found that people with anisometropic amblyopia exhibited

longer latency and less precise saccades when asked to point to a visual target as compared to

visually-normal controls. However, these participants were able attain a similar accuracy and

precision on a simple pointing task during binocular and fellow eye viewing by altering the

kinematics of their pointing movements. Specifically, people with amblyopia prolonged the

acceleration phase and decreased the peak acceleration and velocity during pointing movements

to a visual target under conditions of continuous feedback of limb position in space (Niechwiej-

Szwedo, Goltz, Chandrakumar, Hirji, & Wong, 2011). It was also found that people with

amblyopia displayed reduced precision during the motor planning stage of limb movements, as

evidenced by increased variability of reach during the first 50-100 ms of movement, or in other

words during the early phase of reach trajectory. Additionally, deficits in online correction of

movements were found in patients with severe amblyopia by demonstrating that they displayed

significantly higher end point variability at the terminus of movement, as well as a higher co-

efficient of determination (R2) for the relationship of spatial position of the arm at different

intervals (every 10%) with its final position (Niechwiej-Szwedo, Goltz, Chandrakumar, &

Wong, 2012). Taking these two lines of evidence together, it was surmised that people with

severe amblyopia are relying more on pre-programmed responses during visually-guided

reaching, as opposed to using online visual feedback information to alter and correct the motor

command within the trajectory (Heath, 2005; Niechwiej-Szwedo, Goltz, et al., 2012).

Raashid et al. (2013) found that people with anisometropic amblyopia had a decreased ability to

adapt to an intrasaccadic target step when viewing both binocularly and with the amblyopic eye.

More simply, the authors found that people with amblyopia could not compensate as well as

visually-normal controls for a target that changed positions (jumped ~4˚ backward) after the

initiation of the primary saccade. This was the first indication that the adaptive ability of the

15

amblyopic motor system to a discordant visual signal may be dysfunctional. More specifically,

this study demonstrated that people with amblyopia display decreased saccadic gain, or reduced

ability to change the spatial properties of their saccades during adaptation when the amblyopic

eye is involved (i.e. during binocular and monocular amblyopic eye stimulation). However, a

similar time course of adaptation was observed across viewing conditions as compared to

visually-normal controls, indicating only spatial properties of vision are affected by this

experimental manipulation in amblyopia.

1.3.4 Deficits may be explained by increased internal visual "noise" and spatial undersampling of visual neurons at the striate cortex

Normally, there is a certain amount endogenous noise introduced into all sensory systems that is

compensated for when interacting with the real world (Kelly & O'Connell, 2013). In the case of

vision, normal perception depends on proper noise relations between visual neurons, the amount

of noise present during processing of the sensory stimuli, as well as the number of neurons

recruited for the perception of visual stimuli (Cohen & Newsome, 2009; Shadlen, Britten,

Newsome, & Movshon, 1996). If there abnormal noise correlation between neurons in the visual

system, increased endogenous variability at the processing stages of the visual signal, and/or

spatial undersampling, i.e. a decreased number of recruited neurons after a visual stimulus is

presented, normal visual perception could not occur. Interestingly, it is possible that many of

the deficits observed in amblyopia can be explained by these factors.

Behavioural evidence has demonstrated that introducing dynamic, exogenous noise to the grating

stimuli of visually-normal participants produced elevated contrast thresholds similar to

amblyopic observers (Loshin & Levi, 1983). Additionally, it has been shown that people with

amblyopia display significantly greater fraction of stimulus dependent internal noise as

compared to visually-normal controls (Levi & Klein, 2003). This was demonstrated by a double-

pass experiment where participants performed an identical task two times, and the difference

between the performance on these two trials was taken as a measure of the relative amount of

internal noise. People with amblyopia demonstrated a fraction of internal visual noise of 75%,

where as visually-normal observers displayed one of only ~30%. In the motor domain, longer

latency and less precise saccades in amblyopia have been demonstrated due to slower visual

16

processing visual information from the environment (Niechwiej-Szwedo et al., 2010; Raashid et

al., 2013). Moreover, impaired saccadic adaptation presumably due to decreased precision of the

visual error signal driving adaptation (Raashid et al., 2013), and fixation instability (Gonzalez,

Wong, Niechwiej-Szwedo, Tarita-Nistor, & Steinbach, 2012) have been demonstrated in

amblyopia.

Spatial undersampling has also been proposed as a possible mechanism underlying the deficits

associated with amblyopia. Based on early work demonstrating the shift of OD in artificially

induced amblyopia away from binocularity and the affected eye (Hubel & Wiesel, 1965; Hubel

et al., 1977), it has been suggested that there may be reduced neural representation of responsive

neurons in the striate cortex, or spatial undersampling at V1, of animals reared with the various

amblyogenic factors (Levi, 2013; see section 1.3.2 Classification).

1.3.5 Differences among the amblyopic subtypes

Although all people with amblyopia are subjected to the described deficits (see section 1.3.3

Deficits), the degree to which they are affected and the way in which the decrements manifest

themselves differs across the various subtypes.

People with strabismic and anisometropic amblyopia show differences in the area of the affected

visual field. Strabismic amblyopia displays visual field dependencies with the deficits most

strongly associated with central or foveal vision. In contrast, anisometropic amblyopia is

associated with deficits that are dispersed uniformly across the entire visual field (Hess &

Pointer, 1985; Ikeda & Wright, 1976).

Additionally, people with anisometropic amblyopia display co-varying losses in spatial

resolution whereas strabismic amblyopia does not. For example, when the visual stimulus is

normalized for the amblyopic eye by its grating acuity in anisometropic amblyopia, similar

Vernier acuities in both the amblyopic and fellow eyes are recorded. In contrast, people with

strabismic amblyopia display lower Vernier acuity measurements in the amblyopic eye, even

when normalized for grating acuity. This indicates that there is a scaling affect present in

anisometropic, but not strabismic amblyopia (Levi & Klein, 1982a; McKee et al., 2003).

17

Another discrepancy between the two major types of amblyopia is the spatial localization deficit.

It can be seen that people who suffer from the strabismic subtype display greater difficulty in

spatial localization as measured by the Vernier acuity task. Although people with anisometropic

amblyopia display this deficit as well, it is much more subtle (Levi & Klein, 1982a).

Furthermore, the spatial localization deficit in strabismic amblyopia can be seen in the fellow eye

but this is not the case in the anisometropic subtype (Levi & Klein, 1985).

Finally, many people with the strabismic form of the disease have no stereopsis, whereas people

with anisometropic amblyopia can have some residual binocularity due to peripheral fusion of

the two eyes (Wright, 2006).

1.3.5 Treatment

The gold standard treatment for amblyopia for a number of decades has been patching. Patching

involves occluding the fellow or dominant eye, leaving patients to view the world monocularly

with their amblyopic eye. If successful, patching treatment results in visual acuity improvement

in the affected eye. This has been shown to be most effective in younger children, ages 0-12

years (The Pediatric Eye Disease Investigator Group, 2002), as the visual cortex is still within

the critical and sensitive periods of visual development and is therefore more modifiable (Daw,

1998). In contrast, children aged 13-17 respond to patching treatment to a lesser degree, as

evidenced by a success rate that drops from ~54% to ~25% in the teenage years (The Pediatric

Eye Disease Investigator Group, 2005). Interestingly, there has been increasing evidence that the

brain of people with amblyopia may be plastic well into adult-hood, creating opportunity to treat

people with amblyopia after the critical (and sensitive) period of development has ended (for

comprehensive reviews, see Levi and Li, 2009; Hess and Thompson, 2013). The most

compelling evidence for the ability to improve upon amblyopic deficits in adulthood stems from

dichoptic training with various perceptual learning tasks and video games (Li et al., 2013; Li &

Levi, 2004; Li, Ngo, Nguyen, & Levi, 2011). The perceptual learning and video game tasks

require that both eyes work together to complete the task at hand by modulating the signal to the

fellow eye to make it less or equally reliable to that from the amblyopic eye. If binocularity is

achieved then participants will be successful at completion of the game or task. One noted

example is that of Tetris©

, where participants played the game while wearing anaglyph glasses

and/or used lenticular display. As such, some of the blocks were only presented to the fellow eye

18

and others only to the amblyopic eye. Initially, the contrast of the blocks given to the fellow eye

is low, to allow the amblyopic eye to become equally as reliable as the fellow eye in completion

of the game. Improvements in binocularity were measured by increasing the contrast of the

blocks presented to the fellow eye, i.e. making this signal stronger, and observing that

participants with amblyopia were still able to play the game successfully. Clinical measures of

amblyopia improvement were observed by an overall modest increase in visual acuity of the

amblyopic eye as well as increased stereopsis in amblyopic participants (Li et al., 2013; Li et al.,

2011). This said, there is yet to be a clinical trial to assess the efficacy of the perceptual learning

tasks and/or video games in comparison to patching.

1.3.6 Summary

Amblyopia is a complex disorder resulting in many spatiotemporal deficits along the central

visual pathway, extending from V1 (or even LGN) through the extrastriate visual processing

streams. It is important to characterize all deficits associated with this disorder in order to

understand the true consequences of atypical visual stimulation during early childhood, including

how the amblyopic visual system integrates and adapts to discordance between visual,

proprioceptive and motor information to complete everyday tasks.

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1.4 Visually-guided reaching

The capacity to complete normal visually guided actions depends upon the ability to process

sensory information about the environment and target of interest and the ability to transform

these data into coherent motor commands (Crawford et al., 2004).

Visually-guided reaching requires the cooperation of many different brain structures that control

both sensory and motor signals. Because visually-guided reaching requires the acquisition of an

intended target, a normally functioning visual cortex is required (Karnath & Perenin, 2005). As

visual information is processed at the striate cortex, it is divided along two separate extrastriate

visual streams. Goodale and Milner (1992) asserted that the ventral stream extending from the

primary visual cortex to the temporal lobe is primarily responsible for object recognition,

whereas the dorsal stream ranging from the visual cortex to the parietal lobe is chiefly involved

in the control of visually-guided actions.

Many structures within the parietal lobe, specifically in the region of the posterior parietal cortex,

are involved in the transformation of sensory information in motor action. Broadmann's area 5,

located posterior to the primary somatosensory cortex, is involved in the conversion of the

external visual information obtained by the primary visual cortex about an intended target into

egocentric reference frames to be used by the motor system (Lacquaniti, Guigon, Bianchi,

Ferraina, & Caminiti, 1995). An analogous structure to the parietal reach region (PRR) initially

identified in the monkey brain (for a review see Andersen and Buneo, 2002), is thought to be

involved in the planning of goal-directed limb movements, but is not activated in response to

oculomotor commands such as saccades (Connolly, Andersen, & Goodale, 2003). Other areas

that have been implicated in visually-guided reaching in the region of the parietal cortex include

the medial occipital-parietal junction (mOPJ; Astafiev et al., 2003; Connolly et al., 2003;

Karnath & Perenin, 2005; Prado et al., 2005), the medial intraparietal sulcus (mIPS; Karnath &

Perenin, 2005; Prado et al., 2005) and the dorsal pre-motor cortex (Prado et al., 2005).

The information is then relayed to the frontal cortex which includes the premotor and primary

motor cortices as well as areas involved in motor planning (Barone & Joseph, 1989;

Constantinidis, Franowicz, & Goldman-Rakic, 2001; Tanji & Hoshi, 2001) and to the cerebellum

to coordinate the intended movement signal (Luaute et al., 2009).

20

As can be seen by the information presented above, a normal visual signal being relayed to the

dorsal stream would be necessary to facilitate proper visually-guided action. In the case of

amblyopia, there is abnormal sensory information being relayed to the parietal lobe in addition to

known deficits in processing of information along the dorsal visual stream (Simmers et al., 2003;

Simmers et al., 2006). As such, it is expected that visually-guided reaching should be affected in

this visual disorder (see section 1.3.3.3 Motor deficits). What has not yet been explored is the

effect of abnormal visual-guided actions on the ability to adapt to the manual motor system to a

perturbation in the visual environment in amblyopia. The next section will introduce this idea of

'sensorimotor adaptation', and describe the well-established experimental paradigm of prism

adaptation that was used to induce sensorimotor adaptation in this population in the current

investigation.

21

1.5 Prism Adaptation

The ability to "adapt", or modify our behaviour to an external perturbation is central to

completing everyday activities in the human world (Crawford et al., 2004). Usually, the visual

system is most reliable in detecting subtle differences in the external environment and is

therefore used to direct and calibrate motor movements (Ernst & Bülthoff, 2004).

Based on observations made by lesion studies, any disease which causes abnormal visually

guided reaching, such as cerebellar ataxia, may result in major decrements in quality of life

(Auerbach & Alexander, 1981; Karnath & Perenin, 2005; Lamotte & Acuna, 1978; Perenin &

Vighetto, 1988). As such, many investigators have experimentally manipulated the visual

environment in visually-normal control participants to better understand the adaptive ability in

the fully functional brain. One of the methods often used for this purpose is prism adaptation.

Compensation for a shift of the visual world induced by wedge prisms is a well-established

method of sensorimotor adaptation. It involves modulating the motor system in response to a

shift of the visual world (Fernandez-Ruiz & Diaz, 1999).

1.5.1 Observations during prism adaptation

Wedge prisms induce a shift in the visual field by refracting light at their surface and displacing

it by a fixed value (Figure 1-3). If wedge prisms are placed over the eyes of a participant during a

condition of minimal information, that is there is no information about the prism experimental

manipulation and no visual feedback of limb position, participants will most likely not perceive

any change in the external sensory world (Redding, Rossetti, & Wallace, 2005). Once active

movements towards a target are made and visual feedback of limb position in space becomes

available, participants will notice that they will miss the target in the direction of optical shift. In

the presence of this visual feedback of limb position and after repeated trials, participant will

eventually be able to move accurately towards the target of interest. This adaptation follows an

exponential time course where there is a rapid error correction phase (strategic recalibration),

gradual error correction, and a plateau phase (spatial realignment). The magnitude of prism

adaptation is dependent not on the amount of time exposed to prisms, as evidence by a lack of

adaptation during passive movements (Held, 1965), but rather on the number of interactions

22

between the visual, proprioceptive and motor system in the presence of visual feedback that

occur while prisms are worn (Fernandez-Ruiz & Diaz, 1999). Once the prisms are removed, and

a similar task is repeated, the participant will miss the target in the direction opposite to the

displacement and this error decays exponentially over subsequent pointing trials similar to

adaptation (Field, Shipley, & Cunningham, 1999). This is commonly referred to as the negative

aftereffect.

Figure 1-3: Ray diagram through a wedge prism. θ' indicate angles of refraction.

If a person places a pair of base-right wedge prisms over their eyes a (i.e. shifts the optical world

leftward) and attempts to point to a visual target in the presence of visual feedback of limb

position, they will initially miss the target to the left. After repeated trials, they will eventually

reach an accuracy equivalent to their baseline pointing accuracy prior to adaptation. Once the

prisms are removed, the person will miss the target to the right, the direction opposite to the

prism displacement.

1.5.2 The prism adaptation paradigm

In the classic prism adaptation paradigm, generally there are three measurements that take place:

1) baseline block to assess general motor performance and to use as a comparator to the prism

adaptation and de-adaptation blocks, 2) prism adaptation, where pointing occurs in the presence

of optical displacement and 3) prism de-adaptation. Sometimes a measurement of open-loop

pointing, or pointing in the absence of visual feedback of limb position in space is assessed as a

23

measure of the change in sensorimotor coordination from before to after prism adaptation. Both

de-adaptation and open-loop pointing present information regarding the "negative aftereffect".

1.5.2.1 The negative aftereffect

The negative aftereffect was first described by the groundbreaking work of von Helmholtz in the

late 1800's on the physiological perception of optics (von Helmholtz, 1867). The negative

aftereffect, or more simply “aftereffect”, is directly observable on tasks performed after the

completion of adaptation and the removal of prisms. It describes a condition where a motor error

results in the direction opposite to that induced optical displacement (i.e. to the right after

adapting to left-shifting prisms). It is generally thought of as the most direct way to observe that

prism adaptation has occurred (Weiner, Hallett, & Funkenstein, 1983).

One way to measure the negative aftereffect is to examine the initial pointing errors during the

de-adaptation block. It has been shown previously that the initial pointing error induced by

adaptation is directly correlated to the pointing error during the de-adaptation block, however,

the de-adaptation error is always smaller, indicating it elicits a somewhat different mechanism of

action than adaptation itself (Fernandez-Ruiz & Diaz, 1999).

Another common measure of the negative aftereffect is the difference in accuracy of open-loop

pointing between before and after adaptation (Efstathiou, 1969; Harris, 1963; Hatada, Rossetti, &

Miall, 2006; Hay, Pick, & Ikeda, 1965; Sarri et al., 2008). In this context, the term open loop

refers to a condition where a task is accomplished in the absence of visual feedback of hand

position (Sarri et al., 2008). The difference from before to after adaptation is a direct measure of

the effect of prism adaptation on the total motor-sensory coordination loop (Redding & Wallace,

2006).

1.5.3 What drives sensorimotor adaptation

To ensure accurate movements to an intended target, the central nervous system (CNS) has to be

capable of making online corrections of motor actions as the external sensory environment is

always changing, both within a single action, and across multiple motor movements. One

influential theory describes an internal forward model of sensorimotor integration to allow for

adaptation of the motor system to small perturbations in the outside visual world (Hinder, Riek,

24

Tresilian, de Rugy, & Carson, 2010; Tseng, Diedrichsen, Krakauer, Shadmehr, & Bastian, 2007;

Wolpert, Ghahramani, & Jordan, 1995b). The forward model of sensorimotor integration theory

postulates that the CNS acts as a comparator between a predictive sensory signal and an actual

sensory outcome for a particular motor action (Hinder et al., 2010; Tseng et al., 2007). This

way, the CNS is able to make online corrections, eliminate unwanted sensory perception during

movement and allow for motor learning (Wolpert et al., 1995b).

A well-documented example forward model sensorimotor modulation based on sensory

predictive errors is in the oculomotor system during a saccadic eye movement. An efference

copy (predictive estimate) of the position of the eye in space is generated prior to the initiation of

the saccade. This efference copy is sent to the cerebellum just prior to the actual saccade taking

place. If there is a discrepancy between the actual and predicted outcomes of the eye position,

the forward model is then updated with this information to ensure accurate foveation of an

intended target on subsequent eye movements (Bridgeman, 1995).

In addition to ensuring motor movements remain accurate in everyday life, this model has also

been applied to experimental manipulations during visuomotor adaptation. It can be seen that

any type of adaptation that requires the cerebellum utilizes sensory prediction errors to

compensate for the inharmonious sensory and motor signals sent to the brain (Tseng et al., 2007).

This has been described in saccadic adaptation and prism adaptation.

1.5.3.1 Prism adaptation and sensory prediction errors

Older behavioural studies provide anecdotal evidence that support the hypothesis that sensory

prediction error is crucial in driving prism adaptation. For example, Welch (1969) described an

experiment where the greatest measureable aftereffect was observed during prism adaptation

when participants were asked to point to a visual target, in contrast to pointing to a random

position in space or deliberately to the side of a target. He concluded that a vital factor in the

ability to adapt is the availability of error information between the predicted and actual position

of visual feedback of limb position in space.

When wedge prisms are placed over the eyes, the optical world shifts in the direction opposite to

the base of the prism. Prior to the initiation of the first pointing movement a feedforward

predictive sensory command is sent to the CNS. This predictive estimate approximates where

25

the feedback of limb position is expected to appear once movement is initiated. This estimate

will be close to the position of the perceived (shifted) target. However, once the participant

points to the target, they will miss the veridical position of the target by a similar magnitude to

the optical shift induced by the prisms (Cohen, 1967). When this occurs, a visual error signal is

generated quantifying the difference between the previous forward predictive estimate (estimated

location of visual feedback of limb position) and the actual sensory outcome (actual feedback

position of the hand) for the pointing motion (Tseng et al., 2007). It is this visual error signal

that updates the forward model of the CNS to allow for more accurate pointing movements on

each subsequent trial. When adaptation is complete, the model has been updated to allow the

predicted and actual position of the feedback of limb position to coincide, and appear at a similar

spatial location as the veridical position of the target. Therefore, the normal acquisition and

generation of this visual error signal is absolutely required for visuomotor adaptation to occur in

response to optically displacing wedge prisms. This means that normal predictive estimates must

be generated and interpretation of visual feedback outcomes must be relatively normal to result

in prism adaptation (Harris, 1963; Hinder et al., 2010).

Additionally, it can be seen that the motor system participation is also required to ensure proper

adaptation to a displaced optical environment. Many studies have examined this by observing

the ability of participants to adapt in the presence of active versus passive motor movement. For

example, Held (1965) was the first to show that participants only adapted to wedge prisms if they

were moving their hand under their own will, rather than an experimenter moving it for them.

He argued that input from the sensory system strives to be directly correlated with output from

the motor system by a defined value created during sensorimotor development. Any perturbation

that results in a discordance of these two systems will cause this constant to be modified to adapt

to the new sensorimotor coordinates. However, active movement is required to detect a

discrepancy in the normal constant (Held & Freedman, 1963).

To sum up, it appears as though normally functioning sensory and motor systems are required to

adapt to optical displacement of images induced by wedge prisms. Furthermore, a sensory

prediction estimate, is required to drive the adaptive behaviour in response to optically displacing

wedge prisms.

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1.5.4 Adaptive processes during prism adaptation

Prism adaptation elicits at least three different adaptive processes: strategic recalibration for

rapid error correction, spatial realignment to deal with discordant sensory signals and postural

adjustment to maintain stability in the presence of an altered visual world (Redding et al., 2005;

Redding & Wallace, 1993, 1996, 2003a, 2006).

1.5.4.1 Strategic recalibration

Strategic recalibration or "conscious correction" results in the rapid error reduction observed

during the early phase of prism adaptation (Redding & Wallace, 2006; Welch, 1978). It

constitutes a period of cognitive learning, as it uses error information from previous trials to

allow for a quick improvement in pointing accuracy early in the adaptation process (O'Shea et

al., 2014; Redding & Wallace, 2001, 2003a). It is thought to represent high level spatial

remapping by way of online, feedforward use of visual feedback to update motor commands and

is task-work space specific (O'Shea et al., 2014; Redding & Wallace, 1993; Rossetti, Koga, &

Mano, 1993). This is the strategy used in everyday life that allows for calibrated and accurate

motor movements to an intended target, and is extremely useful in altering motor output to

transient changes in the visuo-haptic environment (Redding & Wallace, 2006). Based on

previous findings in amblyopia literature (Niechwiej-Szwedo, Goltz, et al., 2012), and due to the

fact that this process is reliant on the ability to use visual feedback efficiently to update the next

motor action (O'Shea et al., 2014), it is possible that people with amblyopia will use this

feedback less efficiently than visually-normal controls and require more trials to adapt to

optically displacing wedge prisms (see section 1.3.3.3 Motor deficits).

1.5.4.2 Spatial realignment

Unlike strategic recalibration, spatial realignment is not a conscious process. Generally,

coordinate frames of different modalities (such as vision, proprioceptive, auditory, etc...) work

together such that accurate commands are transformed and integrated across them all. The

creation of these "aligned" reference frames occurs during normal development. Only in

extenuating circumstances, such as disease, development, and experimental intervention (such as

prism adaptation) does realignment become noticeable (Redding & Wallace, 2006).

27

Spatial realignment differs from strategic calibration as it reduces error indirectly by bringing

discordant references frames back into agreement to allow for proper acquisition of sensory

signals across different modalities, and to convert this synergistic signal into coherent motor

commands (Redding & Wallace, 1993, 1996, 2003a).

Prism adaptation results in a discordance of the eye-in-head and hand-in-head reference frames

that are generated during early development (Held & Freedman, 1963). Prism adaptation,

therefore, is an experimental manipulation to test if realignment developed normally during early

childhood. Spatial realignment, also known as "true adaptation" (Welch, 1978), is the process

that brings these visual and proprioceptive spatial maps back onto one coordinate system and

allows them to work together to complete a non task-space specific goal (Redding & Wallace,

2006).

There are three different reference frames that must be realigned in order to accomplish a task

after prism adaptation occurs. These are the eye-head, hand-head and hand-eye coordinate maps

(Redding et al., 2005; Redding & Wallace, 1993, 1996, 2003a, 2003b, 2006). It is possible that

because amblyopia is a disorder of vision, that eye-head and hand-eye coordinate frames should

be most affected during spatial realignment.

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1.5.4.2.1 Eye-head reference frame

The change in the eye-head reference frame after prism adaptation is captured by a measurement

of visual straight ahead. Generally, a small target is moved across the screen and participants

indicate when they believe the target is perceived to be lined up with the center of the body

(Hatada, Rossetti, et al., 2006; Redding & Wallace, 1993, 2006). The difference in this

measurement from before to after adaptation is commonly referred to as "visual shift". It is

believed to be a measure of a change registered eye position from before to after adaptation

(Crawshaw & Craske, 1974). The realignment of this reference frame results in a unique

consequences on visual perception, where participants now assume their visual straight ahead is

shifted in the same direction as optical displacement induced by the prisms, for example to the

left if adaptation was in response to left-shifting prisms (Redding & Wallace, 2006).

Figure 1-4: Expected shift in the eye-head reference frame from before (A) to after (B) prism adaptation in response to left shifting prisms. Dotted lines represent the veridical midline of the participant. Blue targets represent start position. Dotted arrows represent the trajectory of the target. Grey squares represent target end position.

29

1.5.4.2.2 Hand-head reference frame

The change in the hand-head reference frame after prism adaptation is commonly quantified by a

measurement of straight ahead blind pointing. The difference in this reference from before to

after prism adaptation is generally referred to as the "proprioceptive shift". It is believed to be a

measure of a perceptual change in position sense of the joints. The consequence of this change

results in a shift in straight ahead blind pointing in the direction opposite to the optical

displacement imposed by the wedge prisms, for example to the right in response to adaptation

from left shifting prisms (Hatada, Rossetti, et al., 2006; Redding & Wallace, 1993, 1996, 2006).

Figure 1-5: Expected shift in the hand-head reference from before (A) to after (B) prism adaptation in response to left shifting prisms. Dotted arrows represent expected trajectory of the hand. Dotted lines represent the midline of the participant.

30

1.5.4.2.3 Hand-eye reference frame

The total sensorimotor coordination loop is generally measured by pointing to a visual target in

the absence of visual feedback of hand position, or in an open-loop condition (Hatada, Rossetti,

et al., 2006). It is generally of lesser magnitude than the amount of optical displacement, and the

difference in this measurement from before to after adaptation is often referred to as "total shift"

(Redding, 1981; Redding & Wallace, 1990). The change in this reference frame results in a

shift in sensorimotor control in the direction opposite to the optical displacement of the prisms,

for example to the right in response to left-shifting prisms (Harris, 1963; Hatada, Rossetti, et al.,

2006; Held & Hein, 1958; Redding & Wallace, 1993, 1996, 2006).

Figure 1-6: Expected shift in the hand-eye reference frame from before (A) to after (B) prism adaptation in response to left-shifting prisms. Dotted lines represent trajectory of the hand. Grey squares represent the veridical target position.

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1.5.4.2.3 Wilkinson's additivity model

The realignment of the proprioceptive and visual systems during prism adaptation is described

by a linear mathematical system. Wilkinson (1971) formalized this model by demonstrating that

the absolute value of the shift in eye-in-head reference (visual shift; quantified by a measurement

of visual straight ahead) added to the absolute value of the shift in the hand-in-head reference

(proprioceptive shift; measured by straight ahead blind pointing) equals the absolute value of a

measure of the total sensory-motor coordination loop (total shift; determined by open loop

pointing to a visual target) after prism adaptation (equation 1.1).

|Proprioceptive Shift| + |Visual Shift| = |Total Shift| (1.1)

This is an important concept in the context of this thesis, as amblyopia is understood to be

associated with increased visual variability (Levi & Klein, 2003; Raashid et al., 2013).

Integration of sensory modalities is known to occur in a statistically optimal fashion (Ernst &

Banks, 2002). One model for describing such a situation is called the maximum likelihood

estimation (MLE) model. Briefly, the MLE is a weighted average of all sensory modalities

involved in the completion of a specific task, where the most reliable (least variable) modality is

most heavily depended upon. If one modality is degraded and becomes more variable, its

relative weighting within the MLE decreases and becomes less relied upon to complete the task

at hand (Ernst & Banks, 2002). Generally, on spatial tasks such as prism adaptation, vision is

most reliable in detecting differences in the environment and as such is most heavily weighted

within the MLE (Ernst & Banks, 2002). In contrast, vision is degraded in amblyopia making it

less reliable than would be expected in a visually-normal control. As such, it is possible that

there will be a re-weighting of proprioception and vision in amblyopic realignment such that

vision is less heavily depended upon to adapt to optically displacing wedge prisms.

The relative proportions of the visual and proprioceptive shift within Wilkinson's additivity

model are dependent on the type of visual feedback used during adaptation. For example, if

continuous feedback is presented, the subject is able to see their hand throughout the entire

movement path. As such, the position of the hand in space will be modified to realign with the

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visual input (feedback), resulting in large shift in the proprioceptive reference. In contrast, if the

feedback is provided terminally, i.e. at the end of movement, the eyes will be guided by the felt

hand position in space resulting in a greater shift of the visual coordinate system to realign with

this perceived proprioceptive input (Redding & Wallace, 2006). More simply put, the modality

that appears to be less beneficial in completing the task adapts to align with the more useful

sense (Kornheiser, 1976).

1.5.4.3 Postural adjustment

Postural adjustment refers to the actual/perceived change in body position during adaptation to

displacing wedge prisms (Redding & Wallace, 2006). Postural adjustment generally occurs as a

result of inter-sensory bias effects where the influence of one sense results in a perceived/actual

change in body posture to maintain stability in an altered environment (Welch & Warren, 1986).

For example, visual capture is a phenomenon that results in postural adjustment. When the

visual location of a limb is altered in some way, for example by a lateral shift while wearing

prisms, the position of the body is perceived to be where it is visually located, even if this is not

the limbs veridical position in space (Hay et al., 1965).

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1.5.5 Neural correlates of prism adaptation

Several brain structures have been conclusively demonstrated to take part in the prism adaptation

process (Chapman et al., 2010; Danckert, Ferber, & Goodale, 2008; Luaute et al., 2009).

Additionally, the activation of these regions follows a time course similar to that of adaptation,

with different structures activating to different strengths during the different adaptation

processes, namely strategic recalibration and spatial realignment (Chapman et al., 2010; Luaute

et al., 2009).

1.5.5.1 Cerebellum

The cerebellum is a structure located at the posterior of the human brain, just under the occipital

lobe. It plays a major role in coordination of the motor system in both the oculomotor and

manual motor domains during visually-guided reaching (Stein & Glickstein, 1992). The

cerebellum requires normal visual input to allow for calibrated movements to a visual target

(Stein & Glickstein, 1992). If the cerebellum becomes damaged due to an acquired, congenital

or induced lesion, then inaccurate and uncoordinated movements result (for a review see Diener

and Dichgans, 1992).

Due to its importance in regular visually-guided movements, the cerebellum has been of interest

for scientists studying visuomotor adaptation. By creating selective lesions within this structure

in non-human primates (Baizer, Kralj-Hans, & Glickstein, 1999) and observing behaviour in the

acquired/congenital human condition (Fernandez-Ruiz et al., 2007; Martin, Keating, Goodkin,

Bastian, & Thach, 1996) investigators were able to determine that this structure is absolutely

essential for prism adaptation to occur. Participants with any type of cerebellar dysfunction show

reduced or absent compensation in response to wedge prism placement in front of the eyes after

repeated trials with visual feedback of limb position. In other words, these participants display

pointing errors in the direction of optical displacement, for example to the left of the veridical

position of the target in response to left-shifting prisms, even after repeated pointing trials in the

presence of visual feedback of limb position in space.

More recently, using fMRI, Luaute et al. (2009) has demonstrated that the activation of the

cerebellum during prism adaptation outlasts the initial error correction phase of adaptation,

indicating it may play a role both strategic recalibration and spatial realignment. Chapman et al.

34

(2010) substantiated this claim, again through functional imaging, by demonstrating that the

posterior left cerebellum and anterior right cerebellum were preferentially activated during the

first few trials of adaptation, and this activation outlasted the rapid correction of prism induced

errors. Additionally, both Luaute et al. (2009) and Chapman et al. (2010) found an increase in

activation of the cerebellum during the late phase of adaptation after rapid error correction is

complete, during the spatial realignment adaptive process.

1.5.5.2 Parietal lobe

The parietal lobe has been implicated in the ability to adapt to laterally displacing prisms. It has

specifically been associated with the strategic recalibration (rapid error correction) phase of

adaptation. The first evidence for this stemmed from the fact that participants with bilateral

parietal lobe lesions displayed an inability to use cognitive, online corrections to rapidly reduce

prism induced errors during the first few trials after exposure to laterally displacing wedge

prisms (Newport & Jackson, 2006; Pisella et al., 2004).

The posterior parietal cortex (PPC) has been identified as a necessary structure to accurately

adapt to optical displacement. It is well understood in terms of neutral visually-guided

movements to a non-adapting stimulus (Andersen, 1989; Andersen, Essick, & Siegel, 1985;

Behrmann, Geng, & Shomstein, 2004; Caminiti, Ferraina, & Mayer, 1998). The PPC is

responsible for creating a synergistic sensory signal composed of visual, somatosensory, and

vestibular information (Crawford et al., 2004), and transforming this signal into a hand-centered

coordinate frame to send to the frontal lobe and be used by the motor system (Buneo &

Andersen, 2006; Lacquaniti et al., 1995). It is thought to perform a similar function during the

early stages of prism adaptation (Luaute et al., 2009).

The anterior intraparietal sulcus (aIPS), specifically the inferior parietal lobule-supramarginal

gyrus, has been implicated as a chief structure in rapid error detection and correction during the

first few pointing trials after donning wedge prisms (Chapman et al., 2010; Danckert et al., 2008;

Luaute et al., 2009). Clower et al. (1996) demonstrated this by showing that in response to prism

perturbation, there is increased cerebral blood flow in the contralateral IPS to the pointing arm

during the strategic recalibration phase of adaptation. Danckert et al. (2008) later substantiated

this by demonstrating increased activation in the IPS under fMRI during the first three pointing

35

trials of prism adaptation where motor errors are the largest. Additionally, it can be seen that the

activation in this region as measured by MRI decreases as the terminal error between end finger

and target position shrinks, i.e. as adaptation proceeds (Danckert et al., 2008; Luaute et al.,

2009). This finding implies that it is preferentially involved in the strategic recalibration phase

of adaptation (Redding & Wallace, 2001, 2002, 2003a). Having said this, Chapman et al. (2010)

found that although the inferior parietal lobule was activated during rapid error correction, it

attained a state of significantly higher activation during the late phase of adaptation than

compared to baseline measurements. This observation may indicate that it is also involved in the

realignment phase of adaptation.

The angular gyrus has also been implicated in the ability to adapt to lateral displacing optical

wedge prisms. Chapman et al. (2010) suggested that the angular gyrus structure is preferentially

activated in response to the shift from strategic recalibration to spatial realignment. Other

structures within the parietal cortex that have been implicated are the parietal occipital sulcus

(POS; Luaute et al., 2009) and the superior parietal lobule (SPL; Chapman et al., 2010), but have

not been conclusively demonstrated to be primarily responsible for adaptation. For example,

participants with lesions to the SPL have been shown to be unable to adapt to optically

displacing prisms in Newport and Jackson (2006), but have been shown to adapt in Pisella et al.

(2004). Therefore more studies need to be conducted before verifying the role of these structures

in prism adaptation.

1.5.5.3 Temporal lobe

The temporal lobe has been implicated in the ability for the human brain to adapt to visual

displacement induced by wedge prisms. Specifically, it has been found that the superior

temporal gyrus (STG) and superior temporal sulci (STS) are readily activated in response to the

late phase of prism adaptation -- spatial realignment, and not during manual pointing in the

absence of a perturbation (Luaute et al., 2009). This is a novel finding that has yet to be

substantiated in other functional imaging studies (Chapman et al., 2010). Having said this, the

STG has been associated with the spatial awareness deficit involved in hemispatial neglect

(Karnath, Ferber, & Himmelbach, 2001) thus is a plausible correlate of prism adaptation. Wedge

prisms induce a neglect like state in visually-normal controls by resulting in selective attention of

one side of the visual field (Michel et al., 2003).

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1.5.6 Prism adaptation in [other] pathological conditions

1.5.6.1 Cerebellar disease

As can be seen from section 1.5.5 Neural correlates, the cerebellum is absolutely necessary to

accomplish normal coordinated visually-guided movements and to be able to adapt to a

visuomotor perturbation. In cases where there is a genetic disorder of the cerebellum or an

acquired lesion from a stroke, complete adaptation to optical displacement is virtually

impossible.

Studies on patients with cerebellar dysfunction provided the first evidence of this. The results

from these studies vary, from absolute abolition (Baizer et al., 1999; Gauthier, Hofferer, Hoyt, &

Stark, 1979) to impaired adaptation (Martin et al., 1996; Pisella et al., 2005; Weiner et al., 1983).

For example, Fernandez-Ruiz et al. (2007) examined prism adaptation in patients with

spinocerebellar ataxia type 2. Spinocerebellar ataxia type 2 is a genetic disorder, caused by an

autosomal dominant mutation with severe motor coordination deficits including increased

latency of saccadic eye movements, ataxia of the limbs, abnormal posture and action tremors

(Auburger, 2012). In this subset of patients, prism adaptation is significantly impaired as

observable by decreased magnitude and rate of adaptation but interestingly a similar negative

aftereffect to visually-normal controls (Fernandez-Ruiz et al., 2007).

1.5.6.2 Hemispatial neglect

Hemispatial neglect is a disease of selective spatial inattention. In most simple terms, hemispatial

or visual neglect is caused by damage to one hemisphere of the brain. It is characterized by a

chronic lack of attention to items on the contralesional side of the body, i.e. for right hemispheric

damage there is inattention to objects or body parts in the left visual field. More often than not

neglect occurs as a result of a lesion in the right parietal lobe resulting in a lack of attention in the

left visual field (Parton, Malhotra, & Husain, 2004).

Prism adaptation has been used as a treatment for hemispatial neglect. Simply, patients are

adapted to right shifting prisms for extended periods of time. When the prisms are removed the

negative aftereffect results in pointing errors to the left of an intended target. This intervention

37

allows the patients to acknowledge the left side of space, eliminating the spatial inattention

deficit (for a comprehensive review, see Jacquin-Courtois et al., 2013).

While testing prism adaptation as a therapy for neglect, scientists have characterized deficits in

certain domains of the prism adaptation paradigm in hemispatial neglect patients. Michel et al.

(2003) demonstrated, based on anecdotal evidence, that people with hemispatial neglect rely

more on the spatial realignment phase of adaptation, as evidenced by an enhanced negative

aftereffect in these participants. The authors argued that due to the fact that neglect is associated

with a clear cognitive impairment, namely a loss of visual attention to one side of space (Parton

et al., 2004), they display deficits in the strategic recalibration phase of adaptation (dependent on

cognition) and rely more heavily on spatial realignment to adapt to an optically displaced visual

environment.

Similarly, Aimola, Rogers, Kerkhoff, Smith, and Schenk (2012) demonstrated that neglect is

associated with impairments in strategic recalibration during prism adaptation. This was readily

observable by a decreased ability to compensate for prism induced error, i.e. people with neglect

displayed diminished rapid error correction during the initial pointing trials of adaptation. This

said, the authors did not find a pathologically induced after effect in neglect, and argued that this

finding was due to the fact that the strategic recalibration and spatial realignment phases of

adaptation are independent (Aimola et al., 2012). This idea is not a favorable concept throughout

the literature, as recalibration and realignment are generally thought of as related, yet dissociable

events during the prism adaptation process (Redding & Wallace, 1993, 2001, 2002).

1.5.6.3 Optic ataxia

Optic ataxia is a disorder that results as a consequence of a lesion to the parietal cortex, resulting

in an inability to make accurate and precise visually-guided movements (Cavina-Pratesi,

Ietswaart, Humphreys, Lestou, & Milner, 2010). Typically, optic ataxia is associated with

deficits in use of visual feedback to correct limb actions online, or during that specific

movement. Only one case study to date has looked at the effect of optic ataxia on prism

adaptation. Pisella et al. (2004) demonstrated two main findings. Firstly, it was observable that

optic ataxia is associated with an increased time course of adaptation compared to visually

normal controls by showing that the participant with optic ataxia took approximately five trials to

adapt to optically displacing wedge prisms, whereas the visually-normal control group returned

38

to baseline as early as trial two. Secondly, Pisella et al. (2004) demonstrated that this participant

with optic ataxia displayed impairments in the use of online visual feedback to update the motor

command within a single pointing action. The idea that online control was affected in this patient

was shown directly by observing a greater error on the first pointing trial after adaptation begun

as compared to a cohort of visually-normal control participants. More simply, optic ataxia

resulted in a pointing error on the first trial closer to the optical displacement of the prisms as

compared to visually-normal controls. The authors argued that this pathologically increased

initial pointing error was due to impairments in changing limb trajectory online within the first

pointing trial.

1.5.6.4 Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia in the aging population

(Cummings & Cole, 2002). It is most often characterized by a loss of cognitive function. Prism

adaptation in Alzheimer's disease has been examined by two different groups. Weiner et al.

(1983) demonstrated that patients with Alzheimer's disease display a significantly greater

negative aftereffect than visually-normal controls. The authors argue that this finding occurred as

a result of impairments in the cognitive component of prism adaptation, now commonly known

as the strategic recalibration phase (Redding & Wallace, 1993, 2003a), resulting in patients with

AD relying more on non-cognitive processes to adapt to optically displacing wedge prisms (i.e.

spatial realignment). This finding was a similar result and explanation to the anecdotal evidence

demonstrated by Michel et al. (2003) in hemispatial neglect.

In contrast, Paulsen, Butters, Salmon, Heindel, and Swenson (1993) demonstrated that there was

no significant effect of Alzheimer's disease on prism adaptation when compared to visually-

normal controls. This said, the authors admittedly used a cohort of AD patients with mild to

moderate dementia. It is possible that they were not at an advanced enough stage to demonstrate

a similar result to Weiner et al. (1983).

1.5.6.5 Basal ganglia disorders

'Basal ganglia disorders' are a blanket term that describes a heterogeneous group of conditions

that result as a consequence of dysfunction of the basal ganglia (Albin, Young, & Penney, 1989).

The basal ganglia are a compilation of many different subcortical nuclei projecting to various

39

brain structures including the cerebral cortex, thalamus, and brain stem. One of the chief

functions of the basal ganglia is to modulate the activity of efferent neurons connecting the

cerebral cortex to the upper motor neurons in the pre- and motor cortices as well as the brainstem

to aid in the coordination of motor action (Purves, 2012b).

Prism adaptation has been investigated in two specific basal ganglia disorders, namely

Huntington's disease (HD) and Parkinson's disease (PD; Fernandez-Ruiz et al., 2003; Gutierrez-

Garralda et al., 2013; Paulsen et al., 1993; Weiner et al., 1983).

HD is characterized by the selective loss of two component nuclei of the basal ganglia,

specifically the caudate and putamen (Vonsattel et al., 1985). With respect to HD, the results

vary with respect to its effect on prism adaptation. Gutierrez-Garralda et al. (2013) demonstrated

that there was no effect of HD on wedge prism adaptation, but did observe deficits on a reversed

prism task. Additionally, Fernandez-Ruiz et al. (2003) showed that people with HD display a

comparable magnitude and rate of adaptation to visually-normal controls, however they

demonstrate a decreased negative aftereffect. In contrast, Paulsen et al. (1993) observed that

there was abolition of the rapid error correction phase of adaptation in HD patients that was

positively correlated to their dementia on clinical examination. This said, it must be noted that

these three groups of investigators used differing strength of prisms, as well as methods to induce

adaptation (two had their participants throw balls, one was to point to a visual target in a terminal

feedback condition) that may account for the varying results.

PD is characterized by a reduction of dopamine levels in the caudate and putamen as well as a

selective loss of dopamine producing neurons (Gutierrez-Garralda et al., 2013). The effects of

PD on prism adaptation range from demonstrating no observable deficits (Gutierrez-Garralda et

al., 2013) to decreased negative after-effects in PD patients (Fernandez-Ruiz et al., 2003).

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1.5.7 Summary

Prism adaptation is a complex process that involves both strategic and subconscious processes. It

is an interesting and well-established method of visuomotor adaptation that relies on the ability

to generate and properly interpret a visual error signal. Prism adaptation requires the proper

coordination of many brain structures in order to occur, some of which are known to be affected

by amblyopia. As such, it provides a robust experimental model to examine visuomotor

adaptation in the manual motor domain in people with anisometropic amblyopia.

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Chapter 2 Hypotheses and Objectives

This research specifically investigates the adaptive capacity of the amblyopic motor system to a

lateral shift of the visual world, or in other words to a perturbation in the external visual

environment. Amblyopia is a spatiotemporal disorder of vision, resulting in many perceptual,

sensory and motor deficits (see section 1.3.3 Deficits). Although there have been studies that

examine prehension, oculomotor function and saccadic adaptation in amblyopia, the ability for

these people to calibrate and adapt their manual motor system to a changing external visual

stimulus has yet to be investigated.

2.1 Hypotheses

Normal interpretation of a sensory prediction signal, specifically a visual error signal, is

absolutely required for perceptual adaptation to optically displacing wedge prisms (see section

1.5.3 What drives sensorimotor adaptation?). An error must be observable between predicted

and actual sensory outcomes for a given motor command (Tseng et al., 2007). In cases where

the visual system performance is degraded (such as in amblyopia), more variable visual input is

used to establish the visual error signal driving adaptation. It is therefore hypothesized that

people with amblyopia will require more trials to adapt to an optically displaced visual

environment as compared to visually-normal controls due to increased noise in their visual

system (Bonneh et al., 2004; Fronius et al., 2004; Held, 1965; Levi & Harwerth, 1977; McKee et

al., 2003; Wolpert et al., 1995b).

Based on the results observed in saccadic adaptation (Raashid et al., 2013), and that amblyopia is

associated with increased variability in the visual system (Levi & Klein, 2003; Niechwiej-

Szwedo et al., 2010) and increased spatial uncertainty (Birch & Swanson, 2000; Hong, Levi, &

Klein, 1998; Levi, Klein, & Yap, 1987), it is hypothesized that people with anisometropic

amblyopia will exhibit a decreased magnitude of adaptation as compared to visually normal

controls.

Lastly, due to an increase in variability in the visual system of people with amblyopia (Levi &

Harwerth, 1977; Levi & Klein, 1983, 1985; Levi et al., 1987; Loshin & Levi, 1983; Watt &

Hess, 1987) and undersampling of sensory neurons in V1 (Hess & Field, 1994; Hong et al.,

42

1998; Levi & Klein, 1985; Watt & Hess, 1987); see section 1.3.3 Deficits) it is hypothesized that

people with amblyopia will rely less on visual input and more on proprioceptive input during the

prism adaptation task as compared to visually-normal controls. Should there be a reweighting of

vision and proprioception during adaptation, it is hypothesized that there will be a relative

decrease in proprioceptive shift (based on vision during adaptation) and an increase visual shift

(based on proprioception during adaptation; Cohen, 1967; Hatada, Rossetti, et al., 2006; Redding

& Wallace, 1990).

43

Chapter 3 Materials and Methods

3.1 Materials and methods

Eleven visually-normal control participants (five males, age 28 ± 9 years) who had normal or

corrected-to-normal vision (20/20 Snellen visual acuity or better) and seven people with

anisometropic amblyopia (seven females, age 28 ± 8 years) participated in this study. All

participants underwent full orthoptic assessment to quantify their visual acuity, eye alignment,

refractive error and stereopsis. Amblyopia was defined as a visual acuity of 20/25 or worse in the

amblyopic eye, 20/20 or better in the fellow eye, and an interocular difference of at least two

acuity lines on clinical testing. Anisometropic amblyopia was specifically characterized as

amblyopia in the presence of an interocular refractive error difference of at least 1 diopter (D) in

spherical or cylindrical power. Anisometropic amblyopia was chosen for this particular

investigation for three main reasons. Firstly, people with anisometropic amblyopia display

uniform deficits across the entire visual field, whereas people with strabismic amblyopia display

deficits generally confined to the central visual field (Fronius & Sireteanu, 1989; Hess & Pointer,

1985; Ikeda & Wright, 1976). This cohort was chosen because we use both peripheral and

central target presentations. Secondly, people with strabismic amblyopia display an obvious

oculomotor deficit by way of an eye-turn (Simon & Kaw, 2001) which may confound the data.

Lastly, because wedge prisms cause the eyes to move in the direction of optical displacement

(Craske, 1967; Redding & Wallace, 1988; Rock & Campbell, 1975), it is possible that in

strabismic participants the change in relative position of the eyes would cause the eye to move

out of the suppression scotoma resulting in diplopia, or double vision (Holopigian, Blake, &

Greenwald, 1988; Horton, Hocking, & Adams, 1999). To avoid to this potential confounder,

these people were excluded from the study. Other exclusion criteria included decreased visual

acuity due to ocular pathology, prior refractive surgery or neurologic disease. All participants

provided informed consent prior to participation in this investigation. This study was approved

by the Research Ethics Board at the Hospital for Sick Children and conformed to the

requirements stated in the Declaration of Helsinki.

44

Table 3-1: Clinical data for all participants : 11 visually-normal controls (A) and seven people with anisometropic amblyopia (B). Blue acuities designate the amblyopic eye.

3.1.2 Apparatus

The apparatus used was similar to that described previously (Wolpert, Ghahramani, & Jordan,

1995a). Participants were seated 46 cm in front of a large table inclined at 45˚, with their heads

stabilized by a chin rest. Visual targets were presented on a Mitsubishi CRT monitor (20 inch by

15 inch viewing area) at 120 Hz, with the screen mounted above the participant’s head. The

targets were projected from the CRT and reflected off a semi-silvered mirror, which served two

purposes: 1) create virtual targets in the plane of the inclined table; and 2) block the view of the

participant's hand/arm in the darkened environment. An infrared marker (4 mm in diameter) was

placed on the subject’s right index fingertip, which was monitored in real-time using the motion

capture system described below. To reduce unwanted ambient light sources, the computer

monitor was covered by three neutral density filters (ND 0.3, 0.6 and 0.9 log units). The neutral

45

density filters also prevented the lighter background of the screen from providing a visual

reference during any component of the testing procedure. In the tasks where feedback was

required, a 10 mm diameter circle rendered on the CRT monitor and reflected off the semi-

silvered mirror appeared at the position of the infrared marker in real-time as a virtual image in

the plane of the table. It appeared at the terminus of movement, when the finger reached 75% of

the distance to the target along the Y-axis. Additionally, all tasks described below were

performed in a darkened room (Figure 3-1).

Targets were generated by a custom program (written in C++) and presented within the frame of

the computer screen. Each was a small, white square that subtended a visual angle of 0.25˚ at a

viewing distance of 46 cm.

Reaching movements of the right index finger were recorded by a non-invasive motion capture

system (Optotrak Certus system; Northern Digital, Waterloo, Canada; spatial accuracy 0.1 mm,

resolution 0.01 mm, sampling frequency 200 Hz; Figure 3-1). As the OC’s collection rate

exceeded the display rate, updated finger position data were available for each display frame;

superfluous position data (i.e., those generated between frames) were discarded. The OC was

calibrated prior to initiation of the experimental protocol using a three-point digitizing probe.

Real-time output of the finger movement traces from the OC was displayed on a computer and

monitored at all times to ensure tracking was maintained throughout the experiment. A separate

experimenter oversaw real-time feedback of finger or target position.

A force-sensitive resistor (FSR; Tekscan, Boston, MA; 15 mm diameter) was placed on the

inclined table out of the range of the monitor, 18 cm vertically aligned with the bottom left

corner of the inclined table. The FSR was used to initiate each individual trial for all tasks by

pressing it with the left index finger, triggering on the press of the button. The FSR also served to

end each trial by pressing the button a second time. The FSR signal was received by the

experiment computer via the analog-to-digital converter of a ViSaGe system (Cambridge

Research Systems, Rochester, Kent, UK).

46

Figure 3-1: Experimental setup on the virtual surface apparatus (VSA). Visual targets were projected onto a semi-silvered mirror that also serves to block view of the hand. Visual feedback was presented in real-time in the plane of the table. Blue lines represent the sight line of the participants.

All participants were required to wear welder's goggles fitted with 20 diopter bilateral prisms

situated base right, displacing the visual world ~11.4˚ leftward as well as plano lenses. The field

of view for each eye within this mask was approximately 90˚. The field of view for each eye

encompassed the entire range of targets used during the experiment, both in the prism and non-

prism conditions.

47

3.2 Procedure

The procedure implemented for each participant is outlined in Figure 3-2. Specific details for

each task will be described below.

Figure 3-2: Flow chart of the procedure for all participants.

3.2.1 Pointing with feedback (prism adaptation baseline)

In order to ensure that participants were able to perform baseline pointing, to show that the

prisms induced a shift in the optical field, and to evaluate general motor performance on this task

(Fernandez-Ruiz et al., 2007), participants were asked to point to visual targets located at +9˚,

+3˚, 0, -3˚, and -9˚ along a horizontal axis in the presence of visual feedback without prisms. A

metronome set at 80 beats per minute was used to ensure equal pointing durations

(approximately 3 second cycles) across participants and to prevent oblique/secondary

movements. Fifty trials total were recorded. To begin a trial, participants placed their left index

finger on the FSR and right index finger on a coin (a dime) at the center of the inclined table that

acted as a tactile cue to ensure a consistent start position. When prepared, they tapped the FSR

with their left hand and made a pointing movement toward the target with the right hand. As they

approached the target, the feedback light became visible and the target disappeared. When they

perceived that their finger was lined up with the remembered position of the visual target, they

were instructed to tap the FSR once again to end the trial. They then returned to the coin at the

48

center of the table with their right hand and the initiated the subsequent trial by pressing down on

the FSR.

Figure 3-3: Pointing with feedback (prism adaptation baseline) task. Dotted arrow depicts expected trajectory of the hand. Grey square represents the veridical position of the target of interest. White dot represents visual feedback.

3.2.2 Open-loop pointing (total shift task)

This task was performed as described previously (Hatada, Miall, & Rossetti, 2006; Hatada,

Rossetti, et al., 2006; Redding & Wallace, 1993, 1996, 2001, 2003b, 2006; Wilkinson, 1971).

Participants were asked to point to visual targets located at +9˚, +3˚, 0˚, -3˚, and -9˚ along a

horizontal axis in the absence of prismatic displacement as well as visual feedback of limb

position. In this task, the hand was located under the semi-silvered mirror with the finger

feedback light off for the entire duration of movement. Eighty trials were given (16 to each target

position in randomized order) requiring approximately 1.5 seconds per trial (metronome set at 80

beats per minute). To begin a trial, participants placed their left index finger on the FSR and right

index finger on a coin at the center of the inclined table. When prepared, they tapped the FSR

with their left hand to initiate the trial and pointed towards the visual target with their right hand.

Once they perceived that their finger was aligned with the position of the visual target, they were

instructed to tap the FSR once again. They then returned to the coin at the center with their right

49

hand and began again. This task provides two sources of information: 1) a measure of where the

hand is perceived to be in space; and 2) a measure of a complete sensorimotor coordination loop.

As such, this task was performed twice (pre-exposure/baseline and post-prism exposure) with the

difference between the two repetitions measuring the change in motor-sensory coordination

caused by prism adaptation. This difference is commonly termed "total shift" (Redding &

Wallace, 2006).

Figure 3-4: Open loop pointing (total shift task) before (A) and after (B) prism adaptation. Dotted arrows represent expected trajectories of the hand. Grey squares represent the veridical target position.

3.2.3 Visual straight ahead (visual shift task)

The visual straight ahead task was performed as described previously (Hatada, Miall, et al.,

2006; Hatada, Rossetti, et al., 2006; Redding & Wallace, 1993, 1996, 2001, 2003b, 2006;

Wilkinson, 1971). This task required no finger pointing. Rather, participants were asked to make

a judgment of their visual straight ahead. Prior to initiation of this task, the participants were

asked to close their eyes as the experimenter checked that the head was positioned straight in the

chin rest. Subsequently, participants were asked to open their eyes and stare at the position that

appeared to be directly in front of their nose. When prepared, they were instructed to press the

FSR to begin a trial. The trial consisted of the same visual target that appeared in previous tasks.

50

This time it did not appear at the random target positions presented above, but rather appeared at

either the left or right side of the monitor and moved across the screen at 30 mm/s (Hatada,

Rossetti, et al., 2006) which constituted a trial duration of between 5 and 10 seconds depending

on where the participant perceived their visual straight ahead to be (Redding & Wallace, 1993,

1996, 2001). Sixty trials were recorded, randomly assigning the order of 30 left and 30 right start

targets. As the target lined up with the position on the table they were staring at the participants

perceived straight ahead, they were instructed to press the FSR again to end the trial. Every 15

trials, participants were given a break with their eyes closed for 10 seconds. This task was

performed twice (pre-exposure/baseline and post-prism exposure) with the difference between

the two repetitions measuring the change in the eye-in-head reference frame caused by prism

adaptation. This difference is commonly termed the "visual shift" (Redding & Wallace, 2006).

Figure 3-5: Visual straight ahead (visual shift task) before (A) and after (B) prism adaptation. Dotted grey lines represent the veridical midline of the participant. Blue targets represent target start position. Dotted arrows represent the trajectory of the target. Grey squares represent expected end target position.

51

3.2.4 Blind straight ahead pointing (proprioceptive shift task)

The straight ahead blind pointing task was performed as described previously (Hatada, Miall, et

al., 2006; Hatada, Rossetti, et al., 2006; Redding & Wallace, 1993, 1996, 2001, 2003b, 2006;

Wilkinson, 1971). A piece of masking tape oriented horizontally and subtending the entire length

the table was placed approximately 18 cm from the bottom edge. Participants were instructed to

keep their eyes closed and place their right index finger at the position of that piece of tape that

was aligned with the centre of their body. The left finger controlled the beginning and end of

trials by tapping the FSR. Participants pressed the FSR to begin each trial. After initiation, they

were instructed to point straight out from the center of their body until their arm was extended to

a comfortable length (at about eye level). Once the movement was complete, the FSR was tapped

again to end the trial. Each cycle (out and back movement) required approximately 3 seconds,

and was controlled by a metronome set at 80 beats per minute. Every 15 trials, participants were

given a break for 10 seconds and asked to re-center their finger. Once again this task was

performed twice (pre-exposure/baseline and post-prism exposure) with the difference between

the two repetitions measuring the change in the hand-head reference frame caused by prism

adaptation. This difference is commonly termed "proprioceptive shift" (Redding & Wallace,

2006).

Figure 3-6: Straight ahead blind pointing (proprioceptive shift task) before (A) and after (B) prism adaptation. Dotted arrows represent expected trajectory of the hand. Dotted lines represent the midline of the participant.

52

3.2.5 Prism adaptation

The same task as presented in section 3.2.1 Pointing with feedback was repeated in the presence

of 11.4˚ left shifting prisms. Participants were asked to point to targets located at +9˚, +3˚, 0˚, -

3˚, and -9˚ along a horizontal axis in the presence of visual feedback while wearing left-shifting

wedge prisms. A metronome set at 80 beats per minute was used to ensure similar pointing

durations and exposure time to the prism displacement (approximately 3 second cycles) across

participants and to prevent oblique/secondary movements. Two hundred trials total were

recorded in 50 trial blocks. To begin a trial, participants placed their left index finger on the FSR

and right index finger on coin (tactile cue) at the center of the inclined table. When prepared,

participants were instructed to tap the FSR with their left hand and moved straight out to the

target with the right hand. As they approached the target, the feedback light became visible and

the target disappeared when the pointing finger reaches 75% of the distance to the target along

the Y-axis. When they perceived that their finger was lined up with the vertical height of the

target position in the early trials (see Figure 3-7a), and the vertical height and horizontal position

in the later trials (see Figure 3-7b), they were instructed to tap the FSR once again. They then

returned to the coin (tactile cue) at the center with their right hand and began subsequent trials.

Figure 3-7: Prism adaptation task during early (A) and late (B) trials . Triangle in front of the eyes represents the wedge prism. Dotted arrow represents expected hand trajectory. The grey square represents veridical position of the target. White dot represents visual feedback.

53

3.2.6 Prism de-adaptation

The same task as presented above was repeated after adaptation to 11.4˚ left shifting prisms (and

after all post-adaptation "shift" tasks were complete). Participants pointed to visual targets

located at +9˚, +3˚, 0˚, -3˚, and -9˚ along a horizontal axis in the presence of visual feedback and

without prismatic displacement. A metronome set at 80 beats per minute was used to maintain

similar pointing durations (approximately 3 second cycles) across participants and to prevent

oblique/secondary movements. Seventy trials were recorded. To begin a trial, participants placed

their left index finger on the FSR and right index finger on a raised tactile reference point (coin)

at the center of the inclined table. Participants initiated a trial by tapping the FSR with their left

hand and moved straight out to the target with the right hand. As the index finger of the right

hand approached the target, the feedback light became visible and the target disappeared (at 75%

of the distance to the target along the Y-axis). When they perceived that their finger was aligned

with the remembered position of the visual target, they tapped the FSR to indicate the end the

trial. They then returned to the coin at the center with their right hand and began the next trial.

Figure 3-8: Prism de-adaptation task during early (A) and late (B) trials. Dotted arrows represented anticipated trajectory of the hand. Grey squares represented veridical position of the target. White dots represent visual feedback.

54

3.3 Data analysis

The data were post-processed offline using a customized script (in MATLAB, version 7.6.0; The

Mathworks; Natick, MA). Each pointing movement was assessed by a video that showed its

trajectory. Trials that exhibited secondary/oblique movements or where the task was done

incorrectly were eliminated from the analysis. Additionally, outlier analysis was conducted for

the adaptation and de-adaptation tasks by eliminating any trials that lie outside the ~99.9%

confidence interval (mean ± 3SD) of the exponential fit (see section 3.3.1.2.2 Temporal

properties).

All statistical analyses were performed using the Sigma plot 11.0 software package (Systat

Software Inc.; San Jose, California). Homogeneity of variance and normality assumptions were

tested for using the F-test of equality of variance and Shapiro-Wilk test respectively as well as

observing Q-Q plots. Mean ± standard deviation is presented for each outcome measure. All

negative values indicate a trial where the target, center of body or visual straight ahead

(depending on the task) was to the left of the measures veridical position in space.

All outcome measures were analyzed using the x-axis (horizontal axis), as this was the primary

direction of prism perturbation (lateral shift). This is standard practice throughout the prism

adaptation literature. By replicating previous analyses, one is able to compared the outcome of

this study to that of previous investigations (Fernandez-Ruiz & Diaz, 1999; Fernandez-Ruiz et

al., 2003; Fernandez-Ruiz, Hall, Vergara, & Diiaz, 2000; Martin et al., 1996; Pisella et al., 2004;

Redding & Wallace, 2003a, 2006; and others). Additionally, if the vertical axis was chosen for

analysis, it would have given no information about compensation for a laterally displaced optical

environment. Furthermore, Euclidian distance (i.e. radial error) removes any directionality from

the analysis as it is a 'sum of squares' approach. In this way it confounds the magnitude of

adaptation data, as most of the participants in the current study showed oscillations about the

veridical target position (i.e. slightly to the left or right of the target) at the end of adaptation. If

directionality is removed, it may appear that these participants and further away from their

baseline than they actually are in the axis of prism perturbation.

55

Prior to statistical analysis, all outcome measures were converted to degrees of visual angle by

taking the inverse tangent of the target and/or finger position and dividing it by a constant and

pre-set viewing distance of 460 mm.

3.3.1 Primary outcome measures

3.3.1.1 Pointing with feedback (prism adaptation baseline)

The outcome measures for this task included mean pointing error (accuracy) and variable error

(precision).

The mean pointing error, or accuracy, was defined as the mean difference between terminal

finger and veridical target position. This was calculated for each person individually by

averaging the mean pointing error across all 50 baseline block trials. These data were then

averaged for all participants within a group and compared across groups using Student's t-test

(people with amblyopia vs. visually-normal controls).

The variable error, or precision, was defined as mean standard deviation of the pointing accuracy

throughout the baseline block (Fernandez-Ruiz et al., 2007) The standard deviation was

calculated for each person individually using all 50 baseline block trials. These data were then

averaged within a group and compared across groups using Student's t-test (people with

amblyopia vs. visually-normal controls).

3.3.1.2 Prism adaptation and de-adaptation

3.3.1.2.1 Spatial properties

Normalized magnitude of adaptation and de-adaptation were assessed by taking the mean of the

last 10 baseline trials, and subtracting it from the mean of the last 10 trials of the adaptation and

de-adaptation block respectively (Figure 3-9). At the last ten trials of both the adaptation and de-

adaptation block, all participants across both experimental groups had reached the plateau phase

of the exponential function. The magnitude of adaptation was not expected to be 100% in

visually-normal controls, as there is sometimes head rotation in the direction opposite to visual

displacement (Redding & Wallace, 2004). The normalized magnitude of adaptation and de-

56

adaptation were calculated for each participant individually according to the sample calculation

presented in Figure 3-9. These data were then averaged within each group and compared across

groups using Student's t-test where people with amblyopia were compared directly to visually-

normal controls.

Figure 3-9: Sample calculation for normalized magnitude of adaptation for one visually-normal control. The dashed horizontal line represents mean baseline data. The circle represents the trials that were involved in this calculation.

3.3.1.2.2 Temporal properties

Time constants were computed by fitting an exponential rise to maximum function to raw data

for each participant during prism adaptation (equation 3.1; Figure 3-10a) and exponential decay

functions for de-adaptation (equation 3.2).

(3.1)

(3.2)

By definition, the time constant is the number of trials required to reach ~63.21% of total

adaptation, or the steady-state level of the function, and is equal to 1/b, where b is the rise/decay

constant of the exponential function. F represents the pointing accuracy at a given trial, yo

denotes the steady state (asymptotic) level of pointing reached at the end of adaptation and a is

the change in pointing from beginning of the adaptation block to its conclusion.

57

Prism adaptation and de-adaptation were fit well by these exponential functions, where there was

an initial rapid error correction phase, followed by a gradual error correction phase that

eventually reached a plateau at a steady state. Time constants were computed from the

exponential function for each participant individually. The time constants were then averaged

within each group. Subsequently, a comparison was made for this outcome measure between

visually-normal controls and people with anisometropic amblyopia using Student's t-test for the

adaptation and de-adaptation block individually.

Additionally, a second approach to examining the time course of adaptation was undertaken. In

this method, the data were binned in groups of five trials creating a running average over time.

These data were analyzed using a two-way repeated measures analysis of variance (ANOVA)

with Group (two levels: people with amblyopia and visually-normal control) as the between-

subject factor and bin number as the within-subject repeated factors. Significant main effects and

interactions were analyzed by Tukey's HSD post-hoc test (Figure 3-10b).

Figure 3-10: Representative data for one visually-normal control depicting the exponential fit analysis (A) and binning analysis (B) . The dashed horizontal lines depict the mean baseline data. Error bars represent SEM.

58

3.3.1.3 Realignment tasks

Total shift, visual shift and proprioceptive shift were all calculated in the same manner. These

"shifts" were obtained by subtracting the baseline/pre-adaptation measure from the post-

adaptation measure for the total shift, visual shift and proprioceptive shift individually according

to the sample calculation depicted in Figure 3-11 (Hatada, Rossetti, et al., 2006; Redding &

Wallace, 1996, 2006).

Figure 3-11: Sample calculation for the "shifts" in reference frames for one visually-normal control. Visual, proprioceptive and total shift were all calculated in the same way.

There are two components of the Wilkinson's additivity model that must be described.

The first is the relative proportion of the visual, proprioceptive and total shift tasks. This was

done to assess the impact of visual feedback on realignment. In this case, because terminal

feedback is presented, it is presumed that there will be larger visual shift (based on

proprioceptive input during adaptation) and a smaller proprioceptive shift (based on visual input

during adaptation). To assess this statistically, a two-way repeated measures ANOVA was

performed with Group (2 levels: visually-normal controls and people with anisometropic

amblyopia) as the between-subject factor and Shift (3 levels: visual, proprioceptive, and total) as

the within-subject repeated factor. All significant main effects and interactions were analyzed

using Tukey's HSD method.

59

The second aspect to evaluate when assessing whether this model holds in anisometropic

amblyopia is to assess additivity directly. To do this, a separate two-way repeated measures

ANOVA was performed with Group (2 levels: visually-normal controls and people with

anisometropic amblyopia) as the between-subject factor and Shift (2 levels: total and the sum of

the proprioceptive and visual shift) as the within-subject repeated factor. All significant main

effects and interactions were assessed by Tukey's HSD post-hoc method.

These two analyses were conducted separately because of the fact that the proprioceptive and

visual shift alone are not independent of the summed shifts, which is a violation of one of the

assumptions of ANOVA.

60

Chapter 4 Results

4.1 Pointing with feedback (prism adaptation baseline)

Figure 4-1a summarizes the result of the analysis for the comparison of control participants and

people with anisometropic amblyopia for baseline pointing accuracy. There was no significant

differences between the two groups on baseline pointing accuracy (controls = -0.34 ± 0.43˚ vs.

people with anisometropic amblyopia = -0.13 ± 0.24˚; t(16) = -1.18, p = 0.26). Figure 5-12b

depicts the mean standard deviation, or precision, of the two groups on this baseline task.

Visually-normal control participants (1.01 ± 0.29˚) and people with anisometropic amblyopia

(1.00 ± 0.14˚) displayed similar variable error on this type of pointing task (t(16) = 0.08, p = 0.94).

Figure 4-1: Group mean accuracy (A) and precision (B) for the baseline block. Error bars represent SEM.

The effect of target position on pointing accuracy and precision during this baseline task was

also examined. To assess accuracy of pointing movements to the various target positions, the

data were averaged for each target position, and compared across groups. A two-way repeated

measures mixed model ANOVA (between-subject factor: group [visually-normal control vs.

people with amblyopia]; within-subject factor: target position [-9˚, -3˚, 0˚, +3˚, +9]) was used

followed by Tukey's HSD post-hoc test. Figure 4-2a shows the results of the analysis. As can be

seen, there was no significant interaction between Group and Target Position (F (4,64) = 1.44, p =

0.23) and no significant main effect of Group (F(1,16) = 1.12, p = 0.31). This said, there was an

effect of target position (F(4,64) = 9.18, p < 0.001), where pointing accuracy to the -9˚ and +9˚

61

target significantly differed from one another. Additionally, the -9˚ and +9˚ differed

significantly in accuracy of pointing from the +3˚, 0˚ and -3˚ target positions (Figure 4-2a).

The same analysis was carried out for the precision of pointing movements to each target

eccentricity (Figure 4-2b). After subjecting the data to a two-way repeated measures ANOVA

(between-subject factor: Group [visually-normal control vs. people with amblyopia]; within-

subject factor: Target Position [-9˚, -3˚, 0˚, +3˚, +9]) there was no significant main effect of

Group (F(1,16) = 0.45, p = 0.51), Target Position (F(4,64) = 0.94, p = 0.45), or interaction between

these two factors (F(4,64) = 0.98, p = 0.42).

Figure 4-2: Comparison of pointing accuracy (A) and pointing precision (B) to different target positions averaged across all participants. Error bars represent SEM.

4.2 Prism adaptation task

4.2.1 Spatial properties

The magnitude of adaptation was analyzed according to the sample calculation provided above

(see Figure 3-9), where the mean of the last ten trials from the baseline block were subtracted

from the mean of the last ten trials of the adaptation block. Based on this comparison, the mean

normalized magnitude of adaptation for the control group (-0.03 ± 0.81˚) was not significantly

different from our amblyopic cohort (-0.60 ± 0.60˚; t(16) = 1.61, p = 0.13; Figure 4-3).

62

Figure 4-3: Mean normalized magnitude of adaptation for 11 visually-normal controls and seven people with anisometropic amblyopia. Error bars represent SEM.

Although the outcome of the statistical analysis above suggests that there is no significant

difference between the two groups, based on simple examination of the graph it appeared as

though the amblyopic group was much further from their baseline at the end of adaptation. As

such, a simple additional analysis was undertaken to assess how this normalized magnitude of

adaptation compares to zero, where pointing accuracy is expected to be after compensation for

optical displacement. This secondary evaluation was performed as an adjunct to the above

primary analysis due to the fact that the control group displayed a normalized magnitude of

adaptation with variability that crosses the zero line, whereas our experimental group did not.

Two separate one-sample t-tests were conducted comparing both the group consisting of people

with amblyopia and visually-normal control group to zero individually. Based on this analysis, it

can be seen that the anisometropic amblyopia cohort (-0.60 ± 0.60˚; t(6) = -2.64, p = 0.04)

differed significantly from zero, whereas our visually-normal control group did not (-0.03 ±

0.81˚; t(10) = -0.11, p = 0.91).

An additional analysis was conducted to assess the effect of prism adaptation on the initial

pointing error (i.e., the first trial) while wearing wedge prisms. This finding provided

information as to how well the two groups are able to modify pointing trajectories online (within

a single pointing action) using terminal visual feedback (Pisella et al., 2004). As can be seen

(Figure 4-4), there were no significant differences between the two groups on initial pointing

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error (visually-normal controls = -8.5 ± 1.7˚, people with anisometropic amblyopia = -10.7 ±

3.5˚; t(16) = 1.8, p = 0.09). However, it appears as though the mean of the amblyopia group was

closer to the actual optical displacement of the wedge prisms (~11.4˚) than our visually-normal

control cohort.

Figure 4-4: Comparison of the initial pointing error on the first trial of prism adaptation. Dotted line represents actual optical deviation of the wedge prisms (~11.4˚), Error bars represent SEM.

Moreover, the same secondary examination was used as in the baseline pointing condition to

assess the effect of target position on this task. However, instead of using all trials from this

block (as was done in the baseline measure), only the last ten trials for each target eccentricity

were used to assess the accuracy and precision of pointing movements to these various positions

(10 trials per position; 50 total). Using the last ten trials for each target position was done to

ensure there was no effect of optical displacement on pointing accuracy and precision (the last 10

trials encompass the pointing trials where all participants had reached their plateau phase).

To assess the accuracy of pointing movements to the various target positions, the data were

averaged for each target position, and compared across groups. A two-way repeated measures

ANOVA (between-subject factor: Group [visually-normal control vs. people with amblyopia];

within-subject factor: Target Position [-9˚, -3˚, 0˚, +3˚, +9]) was used followed by Tukey's HSD

post-hoc test. Figure 4-5 shows the results of the analysis. There was a significant main effect

of Group (F (1,16) = 5.21, p = 0.04). Additionally, there was a significant effect of Target Position

on pointing accuracy (F(4,64) = 5.25, p = 0.001), where pointing to the +9˚ target differed

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significantly from the -9˚, -3˚, and 0˚ targets. There was no significant interaction between these

two factors (F(4,64) = 0.22, p = 0.93; Figure 4-5).

Figure 4-5: Results of the analysis performed on pointing accuracy to different target positions. (A) Main effect of Group. (B) Main effect of Target Position. Error Bars represent SEM.

The same analysis was carried out for the precision of pointing movements to each target

eccentricity and these data are depicted in Figure 4-6. For each position, the standard deviation

of the data for each participant was calculated and the mean of each group was used in the

analysis. These data were subjected to a two-way repeated measures ANOVA (between-subject

factor: group [visually-normal control vs. people with amblyopia]; within-subject factor: target

position [-9˚, -3˚, 0˚, +3˚, +9]) followed by Tukey's HSD post-hoc test. There is no significant

main effect of Group (F(1,16) = 0.00, p = 0.99). As such, the data displayed in Figure 4-6 have

been pooled across all participants. Additionally, there was no significant main effect of Target

Position (F(4,64) = 1.01, p = 0.40), and no significant interaction between the two factors (F(4,64) =

0.88, p = 0.49).

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Figure 4-6: Precision of movements to the various target positions. Error bars represent SEM.

Due to the fact that the main effect for Group in this model has an F ratio below one, two

separate one-way ANOVAs were run comparing the different target eccentricities in each group

individually. In this analysis, there were no significant differences in endpoint precision when

pointing to the various target positions in either the visually-normal control (F(4,40) = 1.32, p =

0.28) or the anisometropic amblyopia (F(4,24) = 0.71, p = 0.59) groups.

4.2.2 Temporal properties

All participants across both experimental groups displayed an exponential time course of

adaptation. Figure 4-7 depicts the adaptation data for each participant in this study, fitted with

exponential rise to maximum functions. It is noteworthy that generally, the exponential fits

require more trials to asymptote in the group consisting of people with anisometropic amblyopia

than the visually-normal control group.

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Figure 4-7: Exponential fits for 11 visually-normal controls (blue) and seven people with anisometropic amblyopia (red).

69

People with anisometropic amblyopia required significantly more trials (mean = 15 ± 9) to adapt

to left shifting optically displacing wedge prisms than visually-normal controls (mean = 5 ± 4;

t(15) = -3.0, p = 0.008; Figure 4-8). The subject that did not display a time constant significantly

different from zero was excluded from the analysis, due to the fact that this participant’s data

was not fit well by the exponential function (Table 4-1).

Figure 4-8: Comparison of mean time constant values. Error Bars represent SEM.

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Table 4-1: Time constant and R2 values during prism adaptation.

Participant Time Constant 1R

2

C1 2 0.20

C2 1 0.24

C3 4 0.32

C4 8 0.29

C5 2 0.27

C6 2 0.25

C7 4 0.27

C8 2 0.29

C9 9 0.51

C10 4 0.36

C11 16 0.55

A1 10 0.68

A2 12 0.78

A3 19 0.22

A4 7 0.35

A5 32 0.50

A6 10 0.69

A7 291 0.13

1 There was no significant difference between the R

2 values of the two groups (p = 0.28). The pooled mean R

2 of

the two groups (0.38 ± 1.9) was relatively low, presumably due to large dispersion of data surrounding the long

plateau phase of the exponential function.

2 Time constant did not differ significantly from zero (p > 0.05).

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The alternate method used to analyze the time course of adaptation, namely by binning the data

to create a running average of time, resulted in a similar outcome as displayed above. Based on

this analysis, people with anisometropic amblyopia required more trials to adapt to an optical

shift of the visual world than visually-normal controls. There was a significant Group x Bin

Number interaction (F(39,624) = 3.50, p < 0.001), attributable to differences in bins 1, 2, 3, 4, 5, 6,

7 and 11 between the two groups. As can be seen from Figure 4-9, people with anisometropic

amblyopia displayed a more negative, or leftward, average pointing accuracy during the initial

stages of adaptation, indicating they adapted less than visually-normal controls during that

specific subset of five trials.

Figure 4-9: Averaged binned data for adaptation for all participants across the two experimental groups. Each data point represents the average of five trials from the adaptation block. BL represents average baseline accuracy. Error Bars represent SEM.

In order to explain this difference in the time course of adaptation further, an analysis was

conducted to better characterize the rapid error correction phase of the adaptation block. In order

to assess this phase, a calculation of mean of residuals about an estimated linear function fit to

the strategic recalibration phase of the adaptation block was conducted for each participant

individually. Residuals analysis was chosen as it characterizes the variability of points about a

fitted or estimated function.

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A linear function (y = mx+b) was fit to the number of trials equal to the time constant plus two

for each individual participant. This approach was chosen to ensure that the plateau phase does

not contaminate the outcome, as this confounder will increase the value of the mean of residuals

by skewing the fitted line. The one subject that did not show a time constant significantly

different from zero was excluded from the analysis (Table 4-1).

To quantitatively assess the mean of residuals, the equation presented below was used to

calculate the shortest distance for each data point (time constant + 2) to the fitted line for every

participant individually and averaged within groups (equation 4.1).

(4.1)

Where is the shortest distance of the point in question to the fitted line, is the difference

between end finger and target position, is the trial number of that pointing movement, is the

slope of the fitted line, and is the y-intercept of the fitted line. A sample calculation is depicted

below (Figure 4-10).

Figure 4-10: Sample calculation of mean residual distance from linear function for one representative visually-normal control.

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The same analysis presented above was also conducted for the last ten trials of the adaptation

block, where the exponential function had reached its plateau phase. This subsequent analysis

allowed for a direct comparison of the variability in motor output when visual error signals were

large and experimentally induced, i.e. during strategic recalibration, versus when they are similar

to that of baseline pointing, i.e. during spatial realignment, after optical displacement has been

compensated for. This analysis also ensured that there were no differences in variability due to

the prisms themselves, as they were placed over the eyes during all measures used for this

comparison.

In order to evaluate this comparison statistically, a two-way repeated measures ANOVA

(between-subject factor: Group [visually-normal control vs. people with amblyopia]; within-

subject factor: Time [strategic recalibration vs. plateau]) followed by Tukey's HSD post-hoc test.

Based on this analysis, there was a significant interaction between Time x Group (F(1,15) = 17.5, p

< 0.001). As can be seen from Figure 4-11, people with anisometropic amblyopia showed a

significantly higher mean of residuals at the initial stages of adaptation as compared to visually-

normal controls at the strategic recalibration phase of adaptation (p = 0.002), and compared to

themselves during the plateau phase of adaptation (last 10 trials; p = 0.002). Visually-normal

controls did not show a significant difference across the two time points (p = 0.23).

Figure 4-11: Comparison of the mean of residuals at the beginning (time constant + 2) and end of adaptation (last 10 trials of the adaptation block). Error bars represent SEM.

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To assess the effect of variability on the time course of adaptation for all participants, a Pearson's

product-moment correlation was conducted between the time constant and the mean of residuals.

A correlation co-efficient (r) of +0.60 was obtained with a p-value of 0.01 which indicated a

significant, yet moderate, positive relation between the number of trials required to reach 63% of

adaptation (the time constant) and the amount of variability (mean of residuals) during the linear/

rapid error correction phase of adaptation for all participants included in this study. This said, it

must be noted that only ~36% of the variance was explained by this finding (Figure 4-12).

Figure 4-12: Relation between time constant and mean of residuals. Open symbols represent amblyopic participants.

Lastly, to assess the effect of visual acuity on the time constant of adaptation for the group

consisting of people with anisometropic amblyopia, a second Pearson's product-moment

correlation was conducted between the time constant and the visual acuity of the amblyopic eye.

There was no significant relation between these two measures (r = -0.3, p = 0.55).

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4.3 Prism de-adaptation task


Similar to the prism adaptation task, the spatial properties of the prism de-adaptation task were

characterized using the same outcome measures, namely the normalized magnitude of de-

adaptation.

The normalized magnitude of de-adaptation was characterized according to the sample

calculation presented in Figure 3-9. As can be seen in Figure 4-13, the mean normalized

magnitude of de-adaptation for the visually-normal control group (0.51 ± 0.56˚) was not

significantly different from that the people with anisometropic amblyopia cohort (0.13 ± 0.56˚;

t(16) = 1.40, p = 0.18).

Figure 4-13: Comparison of normalized magnitude of de-adaptation between the two groups. Error bars represent SEM.

Although there were no significant differences between the two groups based on this

comparison, it appears as though the control group was functionally further away from their

baseline at the end of adaptation as compared to the people with anisometropic amblyopia group,

whose variability crosses the zero line. As such, an additional simple analysis was conducted to

compare each group individually to zero using a one-sample t-test. Visually-normal control

participants displayed a normalized magnitude of de-adaptation that differs significantly from

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zero (mean = 0.51 ± 0.56˚; t(10) = 3.04, p = 0.01), whereas people with anisometropic amblyopia

did not (mean = 0.13 ± 0.57˚; t(6) = 0.62, p = 0.56).

Subsequently a secondary analysis was completed to compare accuracy and precision of pointing

movements to the different target eccentricities on this task. Figure 4-14a shows the accuracy of

pointing movements to the different target positions on this task and 4-14b depicts the precision.

There was no significant main effect of Group (accuracy: F(1,16) = 2.06, p = 0.17; precision: F(1,16)

= 1.43, p = 0.25) as such the bars displayed in the figure below are pooled across groups.

Additionally, there were no significant interactions between Group x Target Position (accuracy:

F(4,64) = 1.45, p = 0.23; precision F(4,64) = 0.54, p = 0.71). The only source of significance found

was a main effect of target eccentricity on accuracy (F(4,64) = 3.62, p = 0.01) and precision (F(4,64)

= 3.5, p = 0.01), attributable to a difference in pointing accuracy between the +9˚ and -9˚ target

positions and in pointing precision between the -3˚ and the ±9˚ target positions.

Figure 4-14: Pointing accuracy (A) and precision (B) to each target position during the prism adaptation task pooled across groups. Error bars represent SEM.


Figure 4-15 demonstrates the exponential decay functions for all of the participants included in

this study. As can be seen, three visually-normal controls and one person with anisometropic

amblyopia did not show the typical exponential decay function for the de-adaptation block

(Figure 4-15; Table 4-2). Additionally, many of the participants in this study did not show a

time constant for this task that differed significantly from zero (Table 4-2).

*

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Figure 4-15: Exponential decay functions for the de-adaptation block for visually-normal controls (blue) and people with anisometropic amblyopia (red). Dotted lines represent the average baseline pointing accuracy.

80

Table 4-2: Time constant and R2 values during prism de-adaptation.

Participant Time Constant 1R

2

C1 29 0.18

C2 2 0.21

C3 22 0.55

C4 26 0.05

C5 4 0.23

C6 26 0.09

C7 3N/A N/A

C8 3N/A N/A

C9 4 0.21

C10 3N/A N/A

C11 13 0.33

A1 2

40 0.26

A2 2 0.51

A3 4 0.19

A4 3N/A N/A

A5 2 0.28

A6 25 0.12

A7 6 0.55

1 There was no significant difference between the R

2 values of the two groups (p = 0.34). The pooled mean R

2 of

the two groups (0.26 ± 1.6) is relatively low, presumably due to large dispersion/high variability of data surrounding

the long plateau phase of the exponential fit.

2 Time constants were not significantly different from zero (p > 0.05).

3 Could not fit exponential decay functions to the de-adaptation block for these individuals.

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Based on the fact that many of the participants in this study did not show a time constant that

differed significantly from zero, or did not show exponential decay on this task, a comparison

between of the time course of de-adaptation by the exponential became relatively difficult due to

the decreased sample size being used for the statistic. Nonetheless, a comparison between

people with anisometropic amblyopia and controls that showed exponential decay with a

significant time constant was conducted (all other participants were excluded; see Table 4-2). As

can be seen in Figure 4-16, there was no significant difference between the two groups with

respect to time constants of de-adaptation (controls = 6 ± 5 trials, people with amblyopia = 4 ± 2

trials; t(6) = 0.85, p = 0.43).

Figure 4-16: Time constant comparison for the de-adaptation block. Error bars represent SEM.

To confirm that both groups displayed an exponential decay on this task on average, global fits

to this block was created and compared. On average, both groups displayed an exponential time

course of adaptation with similar goodness of fits (R2) and time constant values.

Figure 4-17: Global exponential decay fits for the control (blue) and amblyopia (red) groups during prism de-adaptation. Error bars represent SEM.

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There was no significant main effect of Group (F(1,16) = 1.07, p = 0.32), nor was there a

significant interaction (F(14, 224) = 1.28, p = 0.22) when examining the binned data for the de-

adaptation block. However, there was significant main effect of bin number, where both groups

attained baseline accuracy by bin 3, or approximately 15 trials (F(14, 224) = 14.1, p < 0.001; Figure

4-18).

Figure 4-18: Averaged binned data for de-adaptation for all participants across the two experimental groups. Each data point represents the average of five trials from the de-adaptation block. BL represents average baseline accuracy. Error Bars represent SEM.

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4.4 Comparison of baseline, prism adaptation and de-adaptation

4.4.1 Comparison of movement duration across the three blocks

Due to the fact that there is a well understood trade-off between speed and accuracy during

pointing movements as described by Fitt's law (Fitts, 1954), it is imperative that an assessment of

movement duration be included to ensure that the between-group differences are not the result of

different movement speeds, but rather of visual processing capabilities. Although the metronome

was used to ensure equal duration times and consistent prism exposure, a statistical evaluation of

movement duration is presented here to ensure that there are in fact no differences in the

movement durations across group, as some participants may not have had movement times

consistent with the metronome.

Movement duration was calculated by taking the interval from movement onset until end of

movement. Movement onset was defined as the point where the velocity of the limb exceeded 30

mm/s. Similarly, end movement time was defined as the point where the velocity of the limb fell

below 30 mm/s.

The statistical analysis used a two-way repeated measures ANOVA with Group (2 levels:

visually-normal controls and people with anisometropic amblyopia) as the between-subject

factor and Block (3 levels: baseline, adaptation and de-adaptation) and the within-subject

repeated factor followed by Tukey's HSD post-hoc analysis. Based on this investigation, there

was no significant main effect of Group (F(1,18) = 0.6, p = 0.46), Block (F(2,32) = 0.2, p = 0.8) nor

was there a significant interaction (F(2,32) = 1.3, p = 0.30) where movement time was equal to 490

± 22 ms collapsed across groups and blocks.

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4.4.2 Comparison of the magnitude of adaptation and de-adaptation

There was a significant main effect of Block between the normalized magnitude of adaptation (-

0.2 ± 0.8˚) and de-adaptation (0.4 ± 0.6˚; F(1,16) = 15.99, p = 0.001) as assessed by a two-way

ANOVA with Group (visually-normal controls vs. people with anisometropic amblyopia) as the

between-subject factor and Block (adaptation vs. de-adaptation) as the within-subject factor.

There was no effect of Group (F(1,16) = 3.02, p = 0.10) and no significant interaction between

Group x Block (F(1,16) = 0.38, p = 0.55) found (Figure 4-19).

Figure 4-19: Comparison of the normalized magnitude of adaptation and de-adaptation pooled across groups. Error Bars represent SEM.

Additionally, although these two measures differ significantly in magnitude, they were positively

correlated with one another in the visually-normal control group (r = +0.63, p = 0.04; Figure 4-

20a), similar to what has been described previously (Fernandez-Ruiz & Diaz, 1999). Using

Pearson's product moment correlation, it was demonstrated that this relation could not be

replicated for the anisometropic amblyopia group (r = +0.29, p = 0.52; Figure 4-20b).

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Figure 4-20: Relation between normalized magnitude of adaptation and de-adaptation for visually-normal controls (A) and people with amblyopia (B). There is a significant, positive correlation in the control but not the amblyopia group.

4.5 Realignment tasks

Table 4-3 outlines the shifts in the hand-eye (total), eye-in-head (visual) and hand-in-head

(proprioceptive) reference frames from before to after adaptation according to the sample

calculation presented in Figure 3-10. On average, both the visually-normal controls and people

with anisometropic amblyopia displayed shifts in the expected direction for all three reference

frames (Hatada, Rossetti, et al., 2006; Redding et al., 2005; Redding & Wallace, 1996, 2003a,

2006; Wilkinson, 1971).

When comparing the magnitudes of these shifts across groups, the only significant outcome was

a main effect of Shift (within-subject factor with levels total shift, visual shift and proprioceptive

shift; F(2,32) = 39.3, p < 0.001) where the total shift (5.8 ± 2.0˚) was significantly different from

both the proprioceptive (1.5 ± 3.8˚) and visual shifts (-2.2 ± 2.7˚). More importantly, however,

there was a significant difference between the visual and proprioceptive shift with a larger

absolute magnitude of the visual shift, as expected based on presentation of terminal feedback

(Redding & Wallace, 1990); Figure 4-21). There was no significant interaction observed

between Group x Shift (F(2,32) = 3.1, p = 0.06).

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Figure 4-21: Total, visual and proprioceptive shift pooled across the two groups. . Error bars represent SEM.

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Table 4-3: "shifts" for all of the participants included in this study.

Participant Total1 Visual

2 Proprioceptive

3 |Total|

|Visual| +

|Proprioceptive|

C1 4.0˚ -0.3˚4 4.3˚ 4.0˚ 4.6˚

C2 7.4˚ -7.8˚ -3.0˚5 7.4˚ 10.8˚

C3 6.4˚ -4.2˚ 2.1˚ 6.4˚ 6.4˚

C4 4.1˚ -5.0˚ 1.3˚ 4.1˚ 6.3˚

C5 3.1˚ -1.7˚ 7.3˚ 3.1˚ 9.0˚

C6 3.2˚ -2.8˚ -1.3˚5 3.2˚ 4.0˚

C7 7.1˚ 0.5˚4,5

5.3˚ 7.1˚ 5.8˚

C8 5.7˚ -4.6˚ 6.9˚ 5.7˚ 11.5˚

C9 4.8˚ -2.4˚ -0.3˚4,5

4.8˚ 2.8˚

C10 4.3˚ -7.6˚ -6.4˚5 4.3˚ 14.0˚

C11 8.7˚ -0.3˚4 4.3˚ 8.7˚ 6.1˚

Average 5.3 ± 1.8˚ -3.3 ± 2.8˚ 1.9 ± 4.4 5.3 ± 1.8˚ 7.4 ± 3.5˚

A1 8.0˚ -0.7˚ 5.9˚ 8.0˚ 6.6˚

A2 5.2˚ -1.5˚ -0.3˚4, 5

5.2˚ 1.8˚

A3 7.0˚ 0.9˚5 -1.2˚

5 7.0˚ 2.2˚

A4 10.0˚ -0.9˚ -1.5˚5 10.0˚ 2.4˚

A5 6.6˚ 1.5˚5 -0.1˚

4,5 6.6˚ 1.5˚

A6 3.1˚ -2.1˚ 3.7˚ 3.1˚ 5.8˚

A7 4.3˚ 0.1˚4,5

-2.2˚5 4.3˚ 2.4˚

Average 6.3 ± 2.4˚ -0.4 ± 1.3 0.6 ± 3.0 6.3 ± 2.4˚ 3.2 ± 2.1˚

1 Measure of the total sensorimotor co-ordination loop. Shift is expected to be rightward.

2 Measure of the change in eye-in-head reference frame after adaptation. Shift is expected to be leftward.

3 Measure of the change in hand-in-head reference frame after adaptation. Shift is expected to be rightward.

4 Post-adaptation is not significantly different from the pre-adaptation/baseline measure.

5 Shift is in the unexpected direction.

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4.5.1 Wilkinson's additivity model

To demonstrate the overall additivity model, Figure 4-22 displays the absolute values of the total,

visual, proprioceptive and summed shifts for each group individually, situated next to a figure

adapted from the literature (Redding & Wallace, 2006). Generally, the visually-normal control

group displayed a trend similar to that of the data derived from the literature, albeit with more

variability surrounding the means presumably due to a difference in sample size, whereas the

group composed of people with amblyopia appeared on observation somewhat different.

Figure 4-22: Comparison of realignment aftereffects between literature, control and anisometropic amblyopia values. Error bars represent SEM. Copyright © 2006 by the American Psychological Association. Adapted with permission. The official citation that should be used in referencing this material is (Redding, 2006). The use of APA information does not imply endorsement by APA.

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The second set of analyses completed compared the |total shift| to the |proprioceptive + visual|

shift, with the results displayed in Figure 4-23. From this comparison, a significant interaction

between Group x |Shift| emerged (F(1,16) = 8.6, p = 0.01), attributable to a difference in the

summed shifts between the two groups (visually-normal controls: 7.4 ± 3.5˚, people with

anisometropic amblyopia: 3.2 ± 2.0˚). Moreover, it is important to note that there was also a

significant difference between the total (6.3 ± 2.4˚) and summed shifts (3.2 ± 2.0˚) within the

amblyopic cohort, but this effect was not observed within the visually-normal control group.

Figure 4-23: Comparison of the |total| shift with the |summed| shifts. Error bars represent SEM.

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Chapter 5 Discussion and Future Directions

This investigation is the first to examine the effect of the visual deficits in amblyopia on the

ability to adapt to an optically-displaced visual environment induced by wedge prisms. The

results of this study demonstrate that when viewing binocularly, people with anisometropic

amblyopia: 1) exhibit a longer time course of adaptation in response to a leftward shift of the

visual field; 2) show a similar spatial response to optically-displacing prisms as visually-normal

controls (demonstrated by a similar magnitude of adaptation and total shift task); and 3) do not

show typical additivity, or realignment, of the proprioceptive and visual reference frames from

before to after prism adaptation.

5.1 Justification of the experimental paradigm

Generally, investigators performing these experiments have used a single direction of optical

displacement (Efstathiou, 1969; Fernandez-Ruiz et al., 2007; Harris, 1963; Hatada, Rossetti, et

al., 2006; Hay et al., 1965; Redding & Wallace, 1990, 1993; Welch, 1969), as was done in this

study due to some physiological considerations.

Firstly, it has been demonstrated that pointing to a visual target on the same, or ipsilateral side of

the moving limb differs from the same task when the target is presented in the contralateral

visual field (Carey, Hargreaves, & Goodale, 1996; Fisk & Goodale, 1985). These dissimilarities

exist within kinematic parameters such as reaction time (Berlucchi, Crea, Di Stefano, &

Tassinari, 1977; Carey et al., 1996; Carson, Chua, Elliott, & Goodman, 1990; Stefano, Morelli,

Marzi, & Berlucchi, 1980), peak velocity (Carey et al., 1996; Carson, Goodman, & Elliott,

1992), and total duration of movement (Carey et al., 1996; Carson et al., 1990; Carson et al.,

1992) as well as in endpoint accuracy and precision (Carey et al., 1996; Carson et al., 1990;

Carson et al., 1992). Due to the fact that all participants in this study were right handed

volunteers, placing right shifting prisms over the eyes would result in ipsilateral, uncrossed

movements to the visual targets, whereas left shifting prisms would result in contralateral, or

crossed movements to the visual targets. A single direction of optical shift was chosen to ensure

that the differences listed above did not confound the data. Additionally, this finding may

explain some of the pointing differences in precision and accuracy to the various target positions,

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where generally pointing to the more eccentric targets displayed differential results in our

experimental groups (see Chapter 4 - Results).

Secondly, many previous studies focus on the amelioration of hemispatial neglect by using right

shifting prisms (Jacquin-Courtois et al., 2013; Redding et al., 2005; Sarri et al., 2008). As was

stated earlier, hemispatial neglect results in a loss of visual attention to the left side of space

(Aimola et al., 2012; Parton et al., 2004). By adapting these patients to right-shifting prisms for

extensive periods of time, the aftereffect results in attention being shifted towards the left visual

field. Because there has been so much interest in this clinical application, many studies

examining this deficit have only used right shifting prisms. However, the basic science

underlying the neglect deficit has been studied extensively in visually-normal controls by

attempting to use prism adaptation to induce neglect like symptoms. In these studies,

investigators have used left-shifting prisms to attain neglect like symptoms (bias towards the

right visual field) in healthy controls (Goedert, Leblanc, Tsai, & Barrett, 2010; Michel et al.,

2003). Other pathological conditions such as cerebellar disease (Baizer et al., 1999; Fernandez-

Ruiz et al., 2007; Martin et al., 1996; Pisella et al., 2005; Weiner et al., 1983) have examined

both directions of shifts, but analyzed them in separate models or in separate experiments.

Left-shifting prisms were chosen for this investigation as they have been shown to induce a

larger after-effect in visually-normal controls (Goedert et al., 2010). This concept relies on the

concept that the right hemisphere codes only the left side of space whereas the left hemisphere

contains a representation of both sides of space and can therefore compensate for right

hemispheric damage (Iachini, Ruggiero, Conson, & Trojano, 2009; Weintraub, 1987). This idea

has often been used to explain the fact that generally only right hemispheric lesions result in left

spatial neglect (Weintraub, 1987).

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5.2 The prism adaptation task


When comparing the two participant groups involved in this study, there were no significant

differences on the normalized magnitude of adaptation between them. However, when a one-

sample t-test was conducted, it can be seen that people with amblyopia display a significantly

different normalized magnitude of adaptation from zero, meaning they did not fully compensate

for optical displacement of the visual field as visually-normal controls did. This result may have

occurred for a couple of reasons.

Firstly, as was stated above, people with amblyopia display deficits of spatial localization and

positional accuracy on various types of tasks including alignment accuracy (Bradley & Freeman,

1985; Hess & Holliday, 1992; Levi & Klein, 1982b, 1985, 1990), shape discrimination (Watt &

Hess, 1987), line detection (Levi & Klein, 1990), and spatial interval discrimination (Levi &

Klein, 1990). This decreased positional acuity may be a contributing factor for the difference

between the two groups. Like the temporal deficit associated with amblyopia on this task, it is

possible that the alternative kinematic strategy associated with having developed with abnormal

visual stimulation in amblyopia resulting in a state of normal visually-guided reaching accuracy

(Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, Crawford, et al., 2011) is more resistant to

change in response to an altered visual environment, such as an optically displaced visual world.

This increased resistance to alteration may lead to less accurate pointing movements during the

prism adaptation block. Additionally, this concept can be used to explain the finding that there is

a significant main effect for group on pointing accuracy where the amblyopic population

displays worse accuracy when pointing to visual targets in the presence of wedge prisms during

the last 10 trials of adaptation at each target position, where visual error signals are equivalent to

baseline pointing (see Chapter 4 - Results).

A second possibility to explain the difference found in the magnitude of normalized spatial

adaptation between the two groups is that people with amblyopia have demonstrated ‘mini-

neglect’ on perceptual and motor line-bisection tasks (Thiel & Sireteanu, 2009). In the line

bisection investigation, they demonstrated a slight rightward bias on baseline pointing as

compared to visually-normal controls who generally exhibit ‘pseudo-neglect’, displaying a slight

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leftward bias (Bowers & Heilman, 1980). In our study, it can be seen that during baseline

pointing both participant cohorts display left-shifted baselines. However, it appears upon

examination that the visually-normal control group has a larger leftward bias on baseline

pointing than the group consisting of people with amblyopia. Due to the use of baseline pointing

accuracy in the calculation of normalized magnitude of adaptation, it may be that something

similar to ‘mini-neglect’ during baseline pointing is affecting the people with amblyopia group,

where as pseudo-neglect is affecting the visually-normal control group. This difference in

baseline may lead to the differences observed in magnitude of adaptation.

Other investigations into the prism adaptation paradigm have used the difference between the

initial error on the first trial and last trial of the adaptation block (Fernandez-Ruiz & Diaz, 1999;

Fernandez-Ruiz, Diaz, Aguilar, & Hall-Haro, 2004; Fernandez-Ruiz et al., 2000; Fernandez-Ruiz

et al., 2007) to measure magnitude of adaptation. When using this method to assess the

magnitude of adaptation in the current study, it can be seen that there were no significant

differences between the two groups (t(17) = 0.39, p = 0.7).

Lastly, prisms themselves result in chromatic aberration of light, and these intensity of these

aberrations are dependent on the type of material used to manufacture the optical device

(Marimont, 1994). Although this chromatic aberration is not a major factor in the prisms used

for this investigation, it is possible that the slight aberrations produced in this case affect people

with amblyopia more than our visually-normal control group. It may be that the blur in the

amblyopic eye is worsened by the slight chromatic aberrations produced by the prisms.

Additionally, it has been demonstrated that chromatic aberrations negatively affect contrast

sensitivity (Negishi, Ohnuma, Hirayama, & Noda, 2001). It is well understood that people with

amblyopia already display contrast sensitivity deficits in both the amblyopic (Abrahamsson &

Sjostrand, 1988; Hess & Howell, 1977; Levi & Harwerth, 1977) and fellow eyes (Leguire et al.,

1990; McKee et al., 2003). Therefore, it is possible that this affect of aberrations on contrast

sensitivity may potentiate the deficit already present in these participants. This potentially

enhanced contrast sensitivity deficit may have been a confounding factor in the accuracy and

precision results for pointing during adaptation when the prisms are placed over the eyes,

contributing to the group differences observed upon examination of pointing to the different

target positions.

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It is generally understood that the ability to adapt to an optically displaced visual environment

relies on the generation and proper interpretation of a visual error signal that is purely sensory in

nature (Harris, 1963; Tseng et al., 2007). This sensory prediction error is the driving force

behind prism adaptation, and is generated by a discrepancy between expected and actual visual

feedback of hand position in space for a given pointing movement (Tseng et al., 2007).

Conventionally, these error signals near the beginning of adaptation are experimentally larger

than what is observed during everyday life. Over time, these inaccuracies decrease in magnitude

as an effect of updating the underlying forward model of sensorimotor integration (Hinder et al.,

2010; Wolpert et al., 1995b). This modulation in motor output is based on a visual re-afference

signal that eventually results in pointing movements that are similar in precision and accuracy to

baseline trials, and result in sensory prediction errors similar to those observed in the natural

visuo-motor environment (Fernandez-Ruiz & Diaz, 1999; Redding, 2010).

The development of proper sensorimotor integration depends on exposure history to the natural

world (Welch & Goldstein, 1972), where a correlation is created between the motor action itself

and the sensory outcome of that action (Held & Freedman, 1963; von Holst, 1954; Welch &

Goldstein, 1972). On the first pointing movement in response to an optically displaced visual

environment, the central nervous system receives signals that are weakly correlated as

participants have not been exposed to them previously. Therefore, the formation of new

relations are required between the efferent copy of the motor command and the visual

information that is not already coded in the brains neural networks (Held & Freedman, 1963; von

Holst, 1954; Welch & Goldstein, 1972).

This developmental trajectory of sensorimotor integration becomes important when studying

amblyopia as it is a neurodevelopmental disorder of vision. As such, these participants

presumably develop efferent-afferent (motor-sensory) correlations based upon atypical visual

information, which would undoubtedly affect visuomotor control. It is suggested here that these

abnormal correlations may be more resistant to change than those created during normal visual

development, due to the fact they have to deal with more variable visual information (Levi &

Klein, 2003) and visual spatial undersampling (Hess & Anderson, 1993; Levi, Klein, & Wang,

1994). Because of the increased variability in the re-afferent signal driving the motor command

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(Held, 1965), it may be that people with amblyopia require more trials to efficiently create

correlated input-output (motor-sensory) signals for this new set of visuomotor information. It has

been demonstrated previously, that people with anisometropic amblyopia display similar

precision and accuracy during binocular viewing when pointing to a visual target in a normal

(non-adapted) visual environment (Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, Crawford, et

al., 2011). This result has also been replicated in the present study, as no difference in precision

or accuracy were found between the two groups on the baseline pointing task. Niechwiej-

Szwedo, Goltz, Chandrakumar, Hirji, Crawford, et al. (2011) went further and characterized the

kinematic parameters of these pointing movements to demonstrate that people with this visual

disorder display longer acceleration phases and a lower peak acceleration and velocity for each

motor command. It is possible that people with amblyopia employ an alternative mechanism

over their lifetime of abnormal visual interpretation of the natural environment to deal with the

atypical sensory signals used to drive motor commands. In other words, when the visual error

signal is small, and similar to everyday life, people with amblyopia are able to attain a state of

visually guided reaching to an intended target with normal accuracy and precision due to this

alternate kinematic strategy.

But what happens when the visuo-motor error observed is dissimilar to the natural environment?

At the beginning of adaptation, where the error is large, experimentally induced and unlike the

natural environment, people with anisometropic amblyopia display more variability in their

motor output as compared to their visually-normal counterparts. Additionally it was

demonstrated that people with amblyopia display a significant difference within themselves

when comparing the mean of residuals at the beginning versus at the end of the adaptation block

where the error signal is similar to the normal visuo-motor environment (see Figure 4-11). This

observed increase in variability is a typical finding throughout the sensorimotor literature on

amblyopia. It has been demonstrated that these participants display spatiotemporal deficits in

vision possibly resulting from increased visual noise (Levi & Klein, 2003) and spatial

uncertainty leading to a loss of spatial precision (Levi & Klein, 1983; Levi et al., 1987; McKee et

al., 2003; Watt & Hess, 1987), as well as visual spatial undersampling (Hess & Anderson, 1993;

Hess & Field, 1994; Hong et al., 1998). Additionally, it has been shown that people with

amblyopia display increased noise in the oculomotor system demonstrated by more variable

saccadic amplitudes (Niechwiej-Szwedo et al., 2010) and latencies (Niechwiej-Szwedo et al.,

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2010; Raashid et al., 2013) whenever the amblyopic eye is involved (i.e. during binocular and

amblyopic eye viewing). Moreover, it has been demonstrated that participants with

anisometropic amblyopia display diminished short-term saccadic adaptation when the amblyopic

eye is involved due to a less precise visual error signal driving this behavioural response to a

double-stepping target (Raashid et al., 2013).

It is possible that the strategy adopted to deal with visually-guided reaching in everyday life

(Niechwiej-Szwedo, Goltz, Chandrakumar, Hirji, Crawford, et al., 2011) cannot be modified

efficiently when visual error signals are experimentally induced and large, as in the case at the

beginning of the prism adaptation block. Consequently, the increased variability (Levi & Klein,

2003), spatial uncertainty (Levi & Klein, 1983; Levi et al., 1987; McKee et al., 2003) and spatial

undersampling (Hess & Anderson, 1993; Hess & Field, 1994; Hong et al., 1998) of the visual

system in people with amblyopia becomes more pronounced when they are exposed to a visual

error signal where efferent-afferent correlations are weak, resulting in more imprecise motor

output (Held, 1961). The more variable this visual error signal is, the more iterations will be

necessary to efficiently update the forward model of sensorimotor integration (Hinder et al.,

2010; Wolpert et al., 1995b) to allow for compensation in motor performance for the optically-

displaced visual environment. In other words, if this visual error signal, or re-afferent command,

becomes more variable, more trials will be required to attain a similar magnitude of adaptation as

the visually-normal controls. A significant correlation between the number of trials required to

reach ~63.2% of adaptation and the mean of residuals, or variability, at the beginning of the

adaptation block has been demonstrated (see Figure 4-11).

Lastly, it is possible that the temporal asynchrony (Huang et al., 2012) and integration (Altmann

& Singer, 1986) deficits observed in amblyopia can impact visuomotor function during the prism

adaptation paradigm. When feedback is switched on during the prism adaptation task,

participants are required to identify a change in the visual environment that occurs over time (i.e.

going from no vision of the hand to having a visual feedback signal of limb position in space). It

has been demonstrated that on tasks where participants must identify a change in the visual

environment by way of a temporal cue, such as the detection of a changing square on a

checkerboard pattern (Altmann & Singer, 1986) or detection of a dot flashing 180˚ out of phase

with three adjacent dots (Huang et al., 2012), people with amblyopia display slower reaction

times in identifying such a stimulus. It is possible, that the processing time of the visual feedback

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is delayed in amblyopia, resulting less available information about limb position in space in the

last 25% of movement trajectory. If there is in fact less information when visual feedback of

finger position is available, people with amblyopia may require more trials to process a similar

amount of information as visually-normal controls.

There are only three other instances in the literature where experimental groups have displayed

an increased time course of prism adaptation as compared to visually-normal controls

(Fernandez-Ruiz et al., 2000; Pisella et al., 2004).

Fernandez-Ruiz et al. (2000) demonstrated that prism adaptation is impaired by normal aging.

Specifically it was demonstrated that normal aging results in a longer time course of adaptation

and well as significantly greater negative aftereffect than a group of younger individuals. The

authors of this paper went on to argue that these results were a consequence of impaired motor

control in the aging population. In particular, the authors went onto suggest that the results

obtained may be due to impaired cognition in the aging population resulting in deficits in the

strategic recalibration phase of adaptation. There was no difference in the magnitude of

adaptation or de-adaptation observed. Although these results are similar to those obtained from

the present investigation, there is no evidence in the literature to suggest that there is cognitive

impairment in amblyopia. As such, it appears that impaired strategic motor learning due to a loss

of cognition is probably not the cause of the deficits observed in amblyopia.

In their case study, Pisella et al. (2004) demonstrated a decreased rate of adaptation in a patient

suffering from optic ataxia. It has been demonstrated that optic ataxia results in deficits in online

control of motor action (Battaglia-Mayer & Caminiti, 2002; Cavina-Pratesi et al., 2010). In

addition to an increased time course of prism adaptation in this population, Pisella et al. (2004)

demonstrated that optic ataxia resulted in an initial pointing error closer to the actual optical

displacement of the prisms than in visually-normal controls. The authors argued that the

enhanced initial pointing error was as a result of impaired ability to correct limb trajectory within

a single movement. Interestingly, it has been demonstrated previously that online motor control

is impaired in severe anisometropic amblyopia (Niechwiej-Szwedo, Goltz, et al., 2012). Due to

the fact that the results obtained from this optic ataxia study are similar to that observed in the

present investigation, a systematic analysis of the kinematic data of pointing during prism

adaptation has been undertaken and will be reported in future studies (see section 5.9.1

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Evaluation of the dynamics of pointing during baseline, prism adaptation and prism de-

adaptation).

Fernandez-Ruiz et al. (2007) demonstrated an increased time course of adaptation in

spinocerebellar ataxia type 2 patients (SCA 2). It was concluded that patients with SCA 2 display

deficits in spatial recalibration and not spatial realignment, as the aftereffect was comparable to

visually-normal controls. These findings were similar to what was observed in amblyopia,

however the deficit in amblyopia is presumably due to a reduced fidelity of the visual signal

reaching the cerebellum, rather than dysfunction of the cerebellum itself.

5.2.2.1 Temporal deficits in amblyopia: impaired strategic recalibration?

Prism adaptation is made up of two distinct phases: strategic recalibration for rapid error

correction and spatial realignment, which is a slower subconscious process that occurs after

recalibration is complete (Redding et al., 2005; Welch, 1978; Welch & Warren, 1986).

Strategic recalibration is a cognitive response to prism perturbation, resulting in rapid error

correction during the initial stages of adaptation (Redding & Wallace, 2001, 2003a). Strategic

recalibration signifies a conscious process where participants use cognitive reasoning, either by

side pointing (pointing in the direction of optical displacement away from the target (Redding &

Wallace, 2004), or by online use of visual feedback (Redding & Wallace, 2006), to rapidly

correct for large motor errors after prisms are placed over the eyes. It has been hypothesized that

the strategic recalibration component of the prism adaptation process is controlled by the

posterior parietal cortex due to its association with error correction during everyday life

(Chapman et al., 2010; Clower et al., 1996; Danckert et al., 2008; Luaute et al., 2009;

Mountcastle, Lynch, Georgopoulos, Sakata, & Acuna, 1975). Amblyopia is known to result in

deficits along the parietal lobe in the processing of visual information (Giaschi et al., 1992;

Hayward et al., 2010; Ho & Giaschi, 2006; Simmers et al., 2003; Simmers et al., 2006). Motor

functions that require this region may be impaired in amblyopia as it has been shown that there

are some deficits in movement initiation and execution in both adults and children (Grant et al.,

2007; Suttle et al., 2011). Strategic recalibration is the adaptive process where the deficit was

observed in the above investigation, evidenced by more variable motor output in people with

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anisometropic amblyopia. This finding is presumably due to deficits in processing of visual

information along the parietal lobe that is required for normal visually-guided limb movements.

5.3 Comparison of prism adaptation and de-adaptation

It is understood that the magnitude of adaptation is greater than that of de-adaptation (Fernandez-

Ruiz & Diaz, 1999). In the investigation presented, it can be seen that this result has been

replicated. Based on the calculation performed for a normalized magnitude in the present

investigation, a value closer to zero indicates a greater amount of compensation for optical

displacement or the negative aftereffect in the adaptation and de-adaptation tasks respectively.

More interestingly however is that similar to previous literature (Fernandez-Ruiz & Diaz, 1999),

a significant relation between the magnitude of adaptation and de-adaptation was elucidated for

visually-normal controls, but was absent in people with anisometropic amblyopia. This result

was similar to that found in basal ganglia diseases, namely HD and PD, where these patients

displayed no significant correlation between magnitude of adaptation and de-adaptation

(Fernandez-Ruiz et al., 2003). In this case, HD and PD patients displayed a reduced negative

aftereffect as compared to visually-normal controls. Fernandez-Ruiz et al. (2003) discussed that

this finding was most likely due impairments during spatial realignment. The opposite result,

namely that more dependence on spatial realignment results in an enhanced aftereffect, has been

demonstrated in neglect patients (Michel et al., 2003). In contrast, the present investigation did

not demonstrate a magnitude change in the negative aftereffect across groups, rather it appeared

as though people with amblyopia display similar magnitudes of adaptation and de-adaptation

(Figure 4-20b). It has been argued previously that prism adaptation and de-adaptation, although

related, elicit differential processes in order to occur (Fernandez-Ruiz & Diaz, 1999) yet the

nature of these processes have not been elucidated. Based on the findings in people with

amblyopia, it is possible that the nature of this difference lies somewhere in the use of vision as

the magnitude of adaptation and de-adaptation were similar in the people with amblyopia group

and dissimilar in visually-normal controls.

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5.4 Spatial realignment and Wilkinson's Addivity Model

Spatial realignment is an automatic process which results in the reduction of spatial discordance

of the visual, proprioceptive and motor reference frames (Redding & Wallace, 2006). It thought

to be responsible for the negative aftereffect, and depends on the number of interactions with the

world one has after donning the prisms (Fernandez-Ruiz & Diaz, 1999). It has been suggested

that the realignment phase of prism adaptation is most reliant on the function of the cerebellum

(Luaute et al., 2009; Martin et al., 1996; Pisella et al., 2004). Overall magnitude of spatial

realignment remained unimpaired in people with anisometropic amblyopia during the above

experiment as measured by the total shift task. This implies that, although the visual signal

reaching the cerebellum is more variable in amblyopia, it does result in a similar output after

prism adaptation is complete as compared to visually-normal controls (Levi & Klein, 2003;

Niechwiej-Szwedo et al., 2010).

This study is the first in the prism adaptation literature that specifically examines Wilkinson's

additivity model (Wilkinson, 1971) in a patient population. Here, it was observable that even

though the overall magnitude of the negative aftereffect was similar across groups (a direct

measure of the spatial realignment process), the additivity of the different component reference

frames within Wilkinson's model was impaired in amblyopia. This said, there are some

methodological considerations in visually-normal controls that must be addressed before

discussing the amblyopic result.

5.4.1 Considerations for Additivity in visually-normal controls

It is readily observable from the visually-normal control results (Figure 4-22) that there was a

trend towards over-additivity where the sum of the absolute value of the proprioceptive shift

(change in straight ahead blind pointing) and visual shift (change in perception of visual straight

ahead) was slightly greater than the total shift (change in open-loop pointing). This result has

been addressed in the literature as an effect it of presenting multiple target positions during

adaptation (Redding & Wallace, 1978). Additionally, previous literature has normally performed

the three post-adaptation tasks in the order: visual shift, proprioceptive shift and then total shift

(Wilkinson, 1971). In this study, it was important to determine if the prisms caused a consistent

aftereffect in people with anisometropic amblyopia. The easiest way to assess the magnitude of

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the negative aftereffect was by observing the change in open-loop pointing from before to after

adaptation, so this was always done first. However, it is generally believed that there is normal

decay of adaptation with time (Hamilton & Bossom, 1964). Over-additivity may result as a

consequence of the differential order of tasks performed (total, visual, proprioceptive) than

previously reported (visual, proprioceptive, total).

It has been demonstrated previously that delayed and displaced visual information can impact

motor control (Smith & Bowen, 1980). Smith and Bowen (1980) demonstrated that the delay of

presentation of visual information about limb position in space can impact the adaptation

process, even when this delay is for as little as 66 ms. The set-up used in the investigation

presented in this thesis was performed on a virtual surface apparatus. If the delay in processing

of the visual feedback information approached this 66 ms threshold, it is possible that the prism

adaptation mechanism did not occur naturally, resulting in the inability to replicate the

Wilkinson's additivity model in visually-normal controls. The OC was set to sample at 200 Hz

resulting in a possible delay of 5 ms. The VSG was set to sample at 120 Hz resulting in a

possible delay of 8.3 ms. Other sources that may cause negligible delays in the presentation of

the stimuli could be the Windows operating system itself, and the CRT monitor. If the sum of

these delays are added, the worst case scenario would result in processing time of approximately

15 ms. Although this value does not reach the 66 ms cut-off threshold designated by Smith and

Bowen (1980), it is possible that the delay in presentation could account, at least partially, for the

inability to replicate the additivity model in visually-normal controls.

5.4.2 Considerations for Addivity in anisometropic amblyopia

Although the visually-normal control group replicated the additivity results previously reported

on average (Redding & Wallace, 1988, 2006; Wilkinson, 1971), people with amblyopia

displayed a completely different response. It was predicted that these participants would display

a response to prism perturbation that would have resulted in more reliance on proprioception

than vision to attain normal total sensory-motor re-coordination. However, we observed a

radically different distribution in the population composed of people with anisometropic

amblyopia as compared to visually-normal controls. This finding may be due to the fact that

there is a visual influence on proprioceptive development during infancy. The most compelling

evidence to suggest this stems from the delayed development of proper posture and locomotive

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skills in infants who are born completely blind in both eyes (Prechtl, Cioni, Einspieler, Bos, &

Ferrari, 2001; Tobin, Bozic, Douglas, Greaney, & Ross, 1997). It is thought that during

development, visual feedback is provided to the proprioceptive and vestibular systems to

facilitate proper postural control muscles to attain normal control over limbs, torso, head, ect

(Prechtl et al., 2001). Because amblyopia is a developmental disorder of spatial and temporal

vision, it is possible that proprioception was differentially calibrated in these participants during

infancy, leading to abnormal development of the proprioceptive sensory modality when people

with amblyopia reach adulthood. Due to this fact, it is possible that proprioception in amblyopia

is not equivalent to visually-normal controls, and may even be imprecise in this disorder because

it was calibrated based on abnormal vision during early childhood. If proprioception were

abnormal in people with amblyopia, we might expect the component of realignment based on

proprioception (i.e. the visual shift), to be abnormal. Another possibility to explain this effect is

that there additivity does not hold in a non-"normal" brain, however testing in more patient

populations would be necessary to substantiate this hypothesis. Right now, all that can be

concluded is that additivity is not normal in anisometropic amblyopia.

Interestingly, it has been suggested in the literature that there may be a third component to the

additivity model other than just a proprioceptive and visual shift as initially described (Redding

& Wallace, 1993, 1996; Welch, Choe, & Heinrich, 1974; Wilkinson, 1971). Welch et al. (1974)

found that there was no additivity in 100 participants after the prism adaptation paradigm using

similar measures to what was presented in this study. They found that open-loop pointing

magnitude was 1.8˚ more than the summation of the proprioceptive and visual shifts which was

equal to 3.3˚. From this finding they argued that there must be another component involved in

the realignment of sensory coordinate frames after prism adaptation. The authors hypothesized

that this non-additivity was due to a learned visual-motor response during the rapid error

correction phase, or strategic recalibration phase, of prism adaptation (Welch et al., 1974). It is

possible, because strategic recalibration is presumably affected in amblyopia (see section 5.2.2.1

- temporal deficits in amblyopia: impaired strategic recalibration?), that it impacts the additivity

model differently in people with anisometropic amblyopia than in visually-normal controls.

Lastly, it has been suggested that multisensory integration may be impaired in amblyopia.

Narinesingh, Wan, Goltz, Chandrakumar, and Wong (2014) demonstrated that participants with

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amblyopia display a reduced McGurk effect, a visual-auditory perceptual illusion. During this

experiment, auditory sounds are paired with visual stimuli of a face showing either the same or

different mouth position for that particular phoneme (McGurk & MacDonald, 1976). In

visually-normal participants, when there is a dissociation between the visual and auditory

information presented, they tend to hear a sound that does not match the auditory stimulus, but

they are more likely to hear to sound that matches the visual input (Saint-Amour, De Sanctis,

Molholm, Ritter, & Foxe, 2007). In contrast, people with amblyopia generally display responses

that match the auditory stimulus during these incongruent trials during all three viewing

conditions of binocular, amblyopic and fellow eye viewing. (Narinesingh et al., 2014).

This idea of abnormal multisensory integration poses an interesting question with respect to the

Wilkinson's additivity model. How can we expect normal realignment of visual and

proprioceptive coordinate frames after prism adaptation if there is a suggestion that baseline

multisensory integration, in this case visual-proprioceptive integration, is impaired before any

adaptive processes are completed? In order to assess if baseline integration is impaired in

amblyopia among these modalities, further investigation will be required (see section 5.9 Future

directions).

5.5 Are the findings due to more than just visual acuity?

One of the most common questions asked in amblyopia research is "how do we know that the

observed deficits are due to more than just monocular blur?" This is an interesting question, and

one that needs to be addressed in this complex disorder. Based on results from this study and

previous literature, it is extremely likely that the deficits observed here were the result of the

multifaceted spatiotemporal deficits associated with anisometropic amblyopia rather than just the

diminished visual acuity.

Previously, a study had been conducted to examine the effect of monocular blur on pointing

precision and accuracy in visually-normal controls as compared to people with anisometropic

amblyopia. Niechwiej-Szwedo, Kennedy, et al. (2012) placed lenses over the eyes of a visually-

normal control and blurred their vision to acuity match those of the group consisting of people

with anisometropic amblyopia. Their task was initiated reach-to-touch movements to visual

targets after five hours of monocular blurring, where both binocular and monocular testing was

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completed. Based on this experimental protocol, it was found that there was no effect of

monocular blur on eye-hand coordination and reaching movements in healthy controls. People

with anisometropic amblyopia displayed alternate kinematics on reaching movements on the

identical task, indicating that these differences are attributable to more than just the visual acuity

deficit. It was therefore concluded that visual blur was really just one of the triggers for

anisometropic amblyopia, but the important difference between these participants and visually-

normal controls is the abnormal sensory development that results as a consequence of this blur

during early childhood (Wright, 2006).

Additionally, a correlational analysis was conducted in the present study to assess if there was a

relation between visual acuity and time constants during prism adaptation, as the difference in

time constant values between the two groups was the main and most important finding of this

investigation. There was no significant correlation determined (r = -0.3, p = 0.55), indicating

that there is no relation between these two factors. Additionally, the people with anisometropic

amblyopia who had the best visual acuity displayed time constant values similar to those with the

worst acuity and deepest form of amblyopia who participated in this study.

5.6 Insight into the prism adaptation paradigm

Prism adaptation has been extensively studied for the last century in order to better understand

the mechanisms underlying perceptuomotor control and how the CNS is able to transform

changing visual afferent commands in everyday life into sensible and efficient motor output.

Insight from this investigation has provided more merit to the idea that the sensory prediction

estimate, in this case specifically a visual error signal, is required to induce prism adaptation.

Presumably, the motor system of people with amblyopia is normal, as is their cerebellum. The

fact that there were deficits found in the temporal and spatial aspects of this paradigm suggests

that vision is being used to drive adaptation.

Secondly, it has been demonstrated previously that strategic recalibration and spatial realignment

are dissociable processes (Redding & Wallace, 1993, 1996). This investigation has helped

support this notion, as people with amblyopia display deficits during the strategic recalibration

phase, as observed by an increased time course of adaptation, but display similar negative

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aftereffects (open-loop pointing and magnitude of de-adaptation), indicating a normal magnitude

of spatial realignment (but not spatial realignment in general as there is abnormal additivity).

Lastly, this investigation discussed Wilkinson's additivity model in a population that does not

have normal vision. This finding was very interesting as it is the first study to show that the

additivity model does not hold in this visual disorder, in that there is no apparent linear relation

between the realignment of sensory coordinate frames after adaptation (Redding & Wallace,

1993; Welch et al., 1974; Wilkinson, 1971). More investigation is required in other disorders to

assess if it is just amblyopia that affects additivity. This may also serve as another physiological

example of the "three-component" model of prism adaptation (Welch et al., 1974), but more

investigation is required to confirm this hypothesis.

5.7 Importance of this study

From a clinical perspective, this investigation helped to characterize the adaptive ability of the

amblyopic manual motor system in response to displaced visual information. The

characterization of all deficits associated with amblyopia through basic science research may

lead the way for eventual sensorimotor therapies to serve as adjuncts to current sensory-only

therapies such as patching or pharmacological penalization of the fellow eye.

From a basic science perspective, prism adaptation has been examined in many "top-down"

pathologies such as cerebellar ataxia, where these patients have shown decreased or absent

adaption to wedge prisms (Fernandez-Ruiz et al., 2007; Martin et al., 1996). This investigation is

the first study to directly investigate the influence of "bottom-up" visual disorders such as

amblyopia, where the spatiotemporal deficits are evident as early as the primary visual cortex,

during prism adaptation. Amblyopia can be thought of as a "lesion" at the sensory processing

stage, rather than the motor control stage (as in the cerebellum, basal ganglia or parietal cortex),

and provide insight as to how the visual signal processing within the occipital cortex helps to

control visuomotor function.

106

5.8 Conclusion

In conclusion, this study is the first to examine the effect of prism adaptation in anisometropic

amblyopia. The results demonstrate that participants with anisometropic amblyopia display a

decreased rate of adaptation, a similar magnitude of adaptation, a similar magnitude of the

negative aftereffect, and abnormal additivity of reference frames after adaptation. It is suggested

that people with amblyopia display deficits during the strategic recalibration phase of adaptation.

It was specifically demonstrated that there was increased variability in the strategic recalibration

phase of adaptation in people with amblyopia as compared to visually-normal controls at the

same time point and themselves at the plateau phase of adaptation and decreased rate of

adaptation. It was suggested that these deficits were due to abnormal processing of visual

information along the extra-striate visual streams, increased variability in creating new efferent-

afferent correlations in response to optically displacing wedge prisms and deficits in temporal

integration of vision when visual feedback of limb position is switched on.

5.9 Future directions

There are some questions about visual-proprioceptive-motor integration in amblyopia that are

left open ended at the conclusion of this study. The following section will outline future studies

that will hope to answer some of these remaining questions.

5.9.1 Pointing kinematics during baseline, adaptation & de-adaptation

The chief premise of the above investigation was to understand the ability to adapt the manual

motor system to an altered visual stimulus in people with anisometropic amblyopia. The main

finding that was established from this study is that participants with anisometropic amblyopia

require more trials to adapt to optically displacing wedge prisms. Based on the increased

variability observed in motor output during the rapid, cognitive, strategic recalibration phase of

adaptation, it was suggested that people with amblyopia may display deficits in the ability to

efficiently use visual feedback online to alter motor performance during a single pointing

movement or to use this feedback to update the motor command in subsequent movements. In

other words, it has been suggested that online control of motor functions may be impaired during

prism adaptation in anisometropic amblyopia (Niechwiej-Szwedo, Goltz, et al., 2012).

107

If online motor control in amblyopia is impaired during prism adaptation, we may expect that the

results obtained from this study would be similar to those obtained from prism adaptation in

optic ataxia. In their case study, Pisella et al. (2004) demonstrated that in optic ataxia there is

compensation for optical displacement, albeit at a slower pace. Additionally, it was found that

the initial pointing error on the first trial during adaptation is closer to the actual optical

displacement of the prisms than when performed by visually-normal controls due to the inability

for patients with optic ataxia to use online feedback to alter the pointing command during the

movement. These findings are similar to the behaviour observed in people with anisometropic

amblyopia in the present investigation. However, this is where the comparisons between the

behavioural manifestations of these two pathological processes end - amblyopia is a

neurodevelopmental sensory deficit whereas optic ataxia occurs due to a focal lesion along the

parietal lobule, generally due to a stroke, and is not developmental in its trajectory.

In order to test this hypothesis, it is important to assess directly the ability of the manual motor

system to integrate and receive online modifications during pointing movements, especially

during the prism adaptation task.

O'Shea et al. (2014) demonstrated that there are specific kinematic markers that distinguish the

rapid recalibration and spatial realignment phase of prism adaptation. The most compelling

finding from this paper was that during the early stages of prism adaptation, in this case the first

10 trials, a significant correlation was elucidated between the terminal finger position at the end

of a motor action and the start position of the limb on the subsequent trial. This relation was not

found in the late stages of adaptation (trial 90-100), indicating that during the early phases of

prism adaptation the motor action is updated on a trial-by-trial basis, whereas it is not in the late

stages. Additionally, O'Shea et al. (2014) showed that spatial realignment and strategic

recalibration can be separated by specific kinematic markers within a single pointing trial.

Specifically, it was demonstrated that the peak acceleration was increased during the strategic

recalibration phase (as compared to baseline), indicating that rapid error correction occurs as a

consequence of feedforward motor control. In contrast, the acceleration phase during each

pointing movement of the spatial realignment phase was similar to that of baseline pointing and

it was concluded that this phase relies chiefly on feedback mechanisms.

108

What has yet to be demonstrated is the contribution of online control of motor action during the

two phases of prism adaptation. In order to assess the impact of deficits in online motor control

during prism adaptation in anisometropic amblyopia, and to elucidate if online control

contributes to prism adaptation in visually-normal controls, a correlation co-efficient analysis

will be undertaken. Due to the fact that the feedback of hand position in space was only

presented during the last 25% of movement distance, it must first be determined if any online

control is able to take place. It has been suggested previously that the minimum threshold for the

motor system to exhibit online corrections of limb movements in response to visual stimuli

requires approximately 100ms of visual feedback (Saunders & Knill, 2003). If there is sufficient

time to assess online control in these participants, an examination of the correlation between

finger position when the feedback comes on, i.e. at 25% distance to the target, and at the end of

the trial, i.e. at 0% distance to the target, will be undertaken. A high correlation co-efficient

between these two measures indicates that no online correction has occurred -- the position of the

limb has not been modified within a single pointing trial. In contrast, a low correlation co-

efficient indicates that online modification of limb trajectory has been accomplished (Heath,

2005).

This subsequent kinematic analysis was beyond the scope of the present investigation, as these

data are currently quite variable and thus require a larger sample size in order to assess directly.

This will be carried out in future studies and provide more insight into the dissociation between

recalibration and realignment in general, and discuss how these processes are affected by

abnormal, imprecise visual input.

109

5.9.2 Visual-haptic integration in amblyopia

Abnormal integration of different sensory modalities, namely auditory and visual stimuli, have

been demonstrated previously in amblyopia (Narinesingh et al., 2014). It was suggested above

that perhaps we do not see normal realignment of the visual and proprioceptive coordinate

frames after prism adaptation due to abnormal integration of these modalities at a steady state, or

baseline level. One way to test this concept is to look at visual-haptic integration in this

population.

Haptic perception is generally defined as a combination of cutaneous and kinesthetic afferent

input (Klatzky & Lederman, 2010), and involves coordination between touch and proprioception

which are often thought to be related, yet dissociable senses (Canzoneri, Ferre, & Haggard,

2014).

It has been shown previously on numerous studies in different experimental paradigms that

haptics and vision, like other multisensory integration paradigms, associate in a statistically

optimal fashion and are well fit by the maximum likelihood estimation model (Ernst & Banks,

2002; Ernst & Bülthoff, 2004). Ernst and Banks (2002) demonstrated that on a spatially driven

task, healthy participants rely more on visual information than haptic information. In this

experiment, when asked to distinguish the difference in height between two subsequent visual-

haptic stimuli consisting of a three-dimensional visual percept combined with a haptic force

feedback device, the results were always biased towards the visual estimate of height. Once

visual noise was introduced into the system, the height estimate was pulled away from the visual

percept and driven towards the haptic percept. Similarly, Phillips and Egan (2009) found the

same results using a physical haptic stimulus, rather than a three-dimensional rendition with a

force-feedback device.

As was stated above, amblyopia has long been associated with increased variability in the visual

system (Kiorpes, 2006; Levi & Klein, 2003; Nordmann et al., 1992). As such, it should be

expected that normal, statistically optimal multisensory integration where vision is involved

would be impaired by relying less on this modality. As such, an important experiment to help

interpret the additivity results would be to understand if there are any impairments in baseline

sensory integration resulting in the anomalous additivity results observed.

110

One potential experiment would be very similar to the Helbig and Ernst (2007) study where

each modality was assessed independently to compare to the effect when the senses combine.

This investigation would work well in amblyopia, as there are no stereoscopic representation of

stimuli as in the Ernst and Banks (2002) study or dependence on frequency as in the Phillips and

Egan (2009) investigation as both of stereopsis and frequency are known to be deficient in

amblyopia (Birch, 2013; Levi & Harwerth, 1977). All three stages will involve a two-alternative

forced choice paradigm. In the haptics only task, two similar haptic stimuli will be presented in

sequence and participants will have to answer whether or not they are the same. Using the same

reasoning, the visual only task will involve two stimuli again answering whether they are the

same or different. On the final task used to assess integration, a visual and haptic stimulus will

be presented concurrently, followed by another multimodal stimulus. It will be up to the

participant to answer whether or not the two subsequent presentations are the same stimuli or

different stimuli.

If differences in baseline haptic-visual integration are observed on this task, it may help to

explain the abnormal additivity results observed in amblyopia. This will be an important

investigation to discover how multisensory integration occurs in amblyopia.

5.10 Limitations

The major limitation of this investigation was the number of patients available during the

timeline for thesis completion. Although there were enough participants recruited to result in

sensible outcomes and sufficiently powered statistics for the time course and spatial

characteristics measures presented, more subjects would have been of benefit. For example, the

Wilkinson's additivity model data are quite variable at present, so more people with

anisometropic amblyopia may have been useful in the calculation of these parameters and may

have lead to data that could have been interpreted in a clearer fashion.

111

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